Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue

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Table of Contents

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Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue

In this tutorial, you will learn how to chat with the Gemini Pro generative model through the Google AI Python SDK (software development kit), focusing on creating image classification code using PyTorch. A key aspect of this tutorial is engaging with Gemini Pro as a conversational feedback tool, allowing the model to identify and correct errors in the generated code, and evaluating its effectiveness in this process.

This lesson is the 4th in a 6-part series on Gemini Pro:

1. Introduction to Gemini Pro Vision
2. Image Processing with Gemini Pro
3. Image Classification with Gemini Pro
4. Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue (this tutorial)
5. Lesson 5
6. Lesson 6

To learn how to use the Google AI Python SDK for conversational interactions with Gemini Pro as a feedback tool for generating and refining image classification code in PyTorch, just keep reading.

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Introduction to Chat with Gemini Pro

Welcome to Chat with Gemini Pro, the fourth lesson in our comprehensive series dedicated to unraveling the capabilities and innovations of the Gemini Pro generative model, part of the Google AI Python SDK. In this engaging tutorial, we will delve deeper into the interactive realm of AI by learning how to communicate with Gemini Pro effectively. Our focus will be on the creation of image classification code using PyTorch, leveraging Gemini Pro not just as a tool for code generation but as an intelligent conversational partner capable of providing feedback and identifying and rectifying errors in the generated code. This hands-on approach aims to evaluate Gemini Pro’s effectiveness as a dynamic learning and development assistant.

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Recap of Previous Lessons

In our previous journey, Image Classification with Gemini Pro, we delved into how Gemini Pro excels in generating Python code for image classification using the PyTorch framework, and positioning it against formidable counterparts like ChatGPT-3.5 and Bard. We navigated through the challenges of compatibility and error handling in code generation, revealing areas where Gemini Pro excelled and where it faced hurdles. Our exploration offered a glimpse into the potential of AI in simplifying and enhancing the development process in the realm of image processing.

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Leveraging Conversational AI with Gemini Pro for Coding

In transitioning from our in-depth look at image classification in the previous tutorial, this chapter turns our attention to an innovative aspect of working with Gemini Pro: utilizing its chat functionality. In this unique tutorial, we’re not just focusing on further code generation but delving into how Gemini Pro can act as an interactive conversational partner. This partnership enables us to refine image classification code through dynamic feedback, error identification, and correction processes, leveraging the Google AI Python SDK and the powerful PyTorch framework.

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Exploring Gemini Pro as a Conversational AI

Having explored the capabilities of Gemini Pro with the Google AI Python SDK for generating both image classification and image processing code in our recent tutorials, we’re now poised to challenge its limits further. This phase of our journey puts Gemini Pro to the test, examining its potential to function as a conversational AI. It is akin to the roles ChatGPT and Bard have carved out within the conversational AI landscape, but with a twist specific to code generation. The burning question is: Can Gemini Pro, equipped with its advanced capabilities, engage in meaningful dialogue to not only generate code for image processing and classification tasks but also critically assess and correct any errors within that generated code through conversational interactions?

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What to Expect: Enhancing Code Generation with Gemini Pro’s AI

Join us as we embark on this intriguing exploration. Our mission is to assess Gemini Pro’s proficiency as a conversational partner in the realm of AI-driven code generation. Whether you have been following our series from the beginning or are just diving into the world of AI, this tutorial is crafted to provide you with a comprehensive understanding of how conversational AI can be leveraged to enhance the development process. By engaging Gemini Pro in a dialogue aimed at refining and debugging generated code, we strive to uncover new dimensions of its capabilities. This exploration is not just about testing the waters; it’s about pushing the boundaries of what conversational AI can achieve in the specific context of image classification and processing code generation. Stay tuned for the insights and breakthroughs that lie ahead in this and subsequent chapters of our series.

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Setting Up Gemini Pro for Chat

Continuing from our earlier setup in the tutorial, we will keep utilizing the Google AI Python SDK, which provides access to a range of models, Gemini Pro included.

To acquire your API key, head over to Google MakerSuite and log in using your Google account. After logging in, you will be directed to Google AI Studio, where you can create your API key by following the instructions provided. This key is crucial for accessing the Gemini Pro model and other SDK resources programmatically.

Here, you’ll find an option to generate your API key, as illustrated in Figure 1.

After generating your API key, ensure you copy and store it securely. For those utilizing Google Colab, you have the option to safeguard environment variables, file paths, or keys as private, restricting visibility to only yourself and selected notebooks.

This key is essential for your interactions with the Gemini Pro model, particularly when creating image processing code. Secure storage of your key guarantees uninterrupted access to the capabilities offered by Gemini Pro.

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Exploring Chat Capabilities with Gemini Pro

To illustrate the power of conversational AI in the context of coding, let’s start by exploring the basics of initiating a chat with Gemini Pro. This process involves setting up the necessary environment and sending a simple text prompt to the model, which, in turn, responds in a manner akin to a human assistant.

This initial step is pivotal, establishing a foundation for more intricate dialogues that unveil Gemini Pro’s true capabilities — its adeptness at understanding, analyzing, and correcting code through sustained interaction. Such engagement with Gemini Pro enables developers to move beyond conventional code generation, utilizing the model’s insights to potentially refine their image classification projects, aiming for improved precision and efficiency.

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Preparing Your Development Environment for Gemini Pro

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Step 1: Installing the Google Generative AI Library

We begin with the installation of the google-generativeai library via pip, enabling interaction with Google’s generative models, particularly Gemini Pro, in Python, as demonstrated below:

!pip install -q -U google-generativeai

The google-generativeai library is installed.

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Step 2: Importing Essential Python Packages

import textwrap
import google.generativeai as genai
import google.ai.generativelanguage as glm
from IPython.display import Markdown
from google.colab import userdata

This code snippet starts by importing four crucial Python libraries. The textwrap library is utilized for its capabilities in handling and formatting text, making it invaluable for organizing output. The google.generativeai library, referred to as genai, serves as the primary interface for accessing a suite of AI functionalities, with a particular focus on the Gemini Pro model.

The google.ai.generativelanguage library, or glm, plays a crucial role alongside the google.generativeai library in our code. It introduces the Content and Part methods, essential for organizing chat history into roles and messages, thus facilitating a seamless and dynamic information exchange. This structure not only preserves the conversation’s context but also enriches it with each exchange, ensuring that the model’s responses remain relevant and context-sensitive. This significantly enhances user experience by producing conversations that are both natural and coherent. Ultimately, glm extends the google.generativeai library’s functionality, providing specialized tools for managing complex language-based interactions, from conversational AI to text generation and analysis.

The Markdown function from IPython.display is included to improve the presentation of outputs directly in the Colab notebook, allowing for richer text formatting. Additionally, from google.colab import userdata is integrated to facilitate the use of user data within the notebook, enhancing personalized interaction with the models.

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Step 3: Securely Configuring Your API Key

# Used to securely store your API key
# Or use `os.getenv('GOOGLE_API_KEY')` to fetch an environment variable.
GOOGLE_API_KEY=userdata.get("GEMINI_API_KEY")
genai.configure(api_key=GOOGLE_API_KEY)

This snippet utilizes the userdata module from the google.colab library to securely retrieve the "GEMINI_API_KEY", subsequently assigning it to GOOGLE_API_KEY. As an alternative, one might opt to use os.getenv('GOOGLE_API_KEY') for acquiring the API key from an environment variable.

Following this, genai.configure(api_key=GOOGLE_API_KEY) is executed to configure the GenAI library with the specified API key, thus ensuring authorized access to its features. Employing this method within Google Colab notebooks is advantageous for the secure handling of API keys.

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Listing the Available Generative Models

for m in genai.list_models():
    if "generateContent" in m.supported_generation_methods:
        print(m.name)

To get the list of available generative models, we iterate through the list of models available in the genai library, checking for models that support the generateContent method. We do this by calling genai.list_models(), which retrieves a collection of model objects. Each model object contains various properties, including supported_generation_methods, a list indicating the types of content generation the model supports.

For every model m in the list returned by genai.list_models(), we check if "generateContent" is one of the supported methods listed in m.supported_generation_methods. If it is, the model supports content generation capabilities, and we print the name of the model (m.name).

models/gemini-1.0-pro
models/gemini-1.0-pro-001
models/gemini-1.0-pro-latest
models/gemini-1.0-pro-vision-latest
models/gemini-pro
models/gemini-pro-vision

In our series of tutorials, we’ve previously introduced and explored the capabilities of Gemini Pro and Gemini Pro Vision, the flagship models within the Google Generative AI framework. At the time of our last publication, these models represented the cutting edge of what was available for developers and researchers looking to leverage Google’s AI for their projects.

However, recent updates have significantly expanded the Gemini model lineup, introducing new variants that enhance the scope and specificity of AI applications. Among these additions are four notable models: Gemini 1.0 Pro, Gemini 1.0 Pro 001, Gemini 1.0 Pro latest, and Gemini 1.0 Pro Vision latest. Each of these models brings something unique to the table, further diversifying the tools available for AI-driven content generation.

Particularly of interest is the Gemini-1.0-Pro-001 model. Google describes this model as the premier choice for fine-tuning, highlighting its stability and adaptability for applications such as classification, summarization, and document search. This model’s introduction suggests an emphasis on providing developers with options for customizing AI behavior more closely to their specific needs. If you need more information on this, refer to the Model Tuning Guide.

On the other hand, the Gemini-1.0-Pro-latest and Gemini-1.0-Pro-Vision-latest models signify ongoing improvements and updates to the architecture and capabilities of the Gemini Pro series. The designation “latest” implies that these models incorporate the most recent advancements and optimizations, potentially offering superior performance or features compared to their predecessors.

These new models, especially those supporting the "generateContent" method, represent exciting advancements in the Gemini suite. They not only offer a wider range of options for developers and researchers but also signify Google’s commitment to continuous improvement and expansion of their AI capabilities.

Amidst the exciting updates to the Gemini model lineup, with new models enhancing AI applications, our journey through AI-driven content generation continues to evolve. Notably, models like Gemini-1.0-Pro-001 offer specialized opportunities for fine-tuning, emphasizing Google’s push towards customizable AI solutions. Despite these advancements, for today’s lesson, we’ll stick with the familiar Gemini Pro model from our previous tutorials. This approach allows us to build on established knowledge and focus on enhancing our skills in conversational AI and code generation within a consistent framework.

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Loading the Gemini Pro Model

model = genai.GenerativeModel("gemini-pro")

Next, we instantiate the GenerativeModel class from the genai library, specifically selecting the "gemini-pro" model for initialization. By doing so, we allocate the capabilities of the Gemini Pro model to the model variable, thereby facilitating its use in diverse AI-centric tasks, such as text generation and data analysis. This initialization is key to accessing and employing the broad functionalities of Gemini Pro within our code.

In this instance, we adhere to the GenerativeModel‘s default configurations by not defining any optional parameters like generation_config and safety_settings. This decision streamlines our setup process, making it straightforward to engage with the model’s inherent configurations for our applications.

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Enhancing Code Presentation with Markdown

def to_markdown(text):
    text = text.replace("•", "  *")
    return Markdown(textwrap.indent(text, "> ", predicate=lambda _: True))

Next, we have the to_markdown helper function, which transforms a string into Markdown format, ideal for Jupyter notebooks. It starts by converting bullet points into Markdown’s asterisk syntax, followed by indenting each line with a blockquote symbol using textwrap.indent, applying this uniformly across all lines.

The final output is a Markdown object, well-suited for display in Markdown-compatible environments. This enhances the text’s presentation, making it more suitable for environments like Jupyter notebooks that support Markdown rendering.

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Start a Chat Session with Gemini Pro

chat = model.start_chat()

response = chat.send_message(
    """As a culinary expert and food critic who utilizes AI to discover new flavor combinations,
what is an innovative dish combining traditional Italian and Japanese cuisine you would recommend? Please describe its key ingredients and preparation method.
"""
)

to_markdown(response.text)

Here, we initiate a chat with Gemini Pro, where the user, posing as a culinary expert and food critic interested in AI for discovering new flavor combinations, asks for an innovative dish that blends traditional Italian and Japanese cuisine.

This prompt sets the stage for Gemini Pro to generate a creative and detailed response.

Dish Name: Umami Truffle Carbonara

Key Ingredients:
Spaghetti or linguine pasta
Truffle oil
Parmigiano-Reggiano cheese
Pancetta or guanciale
Japanese soy sauce
Mirin
Sake
Egg yolks
Dashi (optional)


Preparation Method:

Prepare the Pasta: Cook the pasta in salted boiling water according to the package instructions. Reserve a cup of the pasta cooking liquid.

Fry the Pancetta: In a large skillet, fry the pancetta or guanciale until crispy. Remove the pancetta and set aside.

Make the Truffle Carbonara Sauce: In a separate bowl, whisk together the egg yolks, grated Parmigiano-Reggiano cheese, truffle oil, and reserved pasta cooking liquid.

Combine the Pasta and Sauce: Drain the pasta and immediately add it to the pancetta skillet. Pour the truffle carbonara sauce over the pasta and toss well.

Add Japanese Flavors: Drizzle with soy sauce, mirin, and sake. Toss to incorporate.

Garnish: Serve hot, garnished with additional grated Parmigiano-Reggiano cheese and truffle oil. For an optional touch, add a few drops of dashi to enhance the umami flavor.


Tasting Notes:
This innovative dish combines the rich and creamy flavors of Italian carbonara sauce with the savory and umami notes of Japanese cuisine. The truffle oil adds an aromatic and earthy element, while the soy sauce, mirin, and sake balance out the richness with a touch of sweetness and acidity. The dashi adds an umami boost, enhancing the overall depth of flavor.

Gemini Pro responds with a detailed description of “Umami Truffle Carbonara,” a dish that creatively merges Italian and Japanese elements. The response includes key ingredients and a step-by-step preparation method, showcasing Gemini Pro’s ability to generate comprehensive and innovative culinary recommendations.

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Retrieving the Chat Session History

print(chat.history)

This code block retrieves and prints the chat history, illustrating the conversational exchange between the user and Gemini Pro. It highlights the chat session’s multi-turn capability, where the model remembers and builds upon previous interactions.

[parts {
  text: "As a culinary expert and food critic who utilizes AI to discover new flavor combinations,\nwhat is an innovative dish combining traditional Italian and Japanese cuisine you would recommend? Please describe its key ingredients and preparation method.\n"
}
role: "user"
, parts {
  text: "**Dish Name:** Umami Truffle Carbonara\n\n**Key Ingredients:**\n\n* Spaghetti or linguine pasta\n* Truffle oil\n* Parmigiano-Reggiano cheese\n* Pancetta or guanciale\n* Japanese soy sauce\n* Mirin\n* Sake\n* Egg yolks\n* Dashi (optional)\n\n**Preparation Method:**\n\n1. **Prepare the Pasta:** Cook the pasta in salted boiling water according to the package instructions. Reserve a cup of the pasta cooking liquid.\n2. **Fry the Pancetta:** In a large skillet, fry the pancetta or guanciale until crispy. Remove the pancetta and set aside.\n3. **Make the Truffle Carbonara Sauce:** In a separate bowl, whisk together the egg yolks, grated Parmigiano-Reggiano cheese, truffle oil, and reserved pasta cooking liquid.\n4. **Combine the Pasta and Sauce:** Drain the pasta and immediately add it to the pancetta skillet. Pour the truffle carbonara sauce over the pasta and toss well.\n5. **Add Japanese Flavors:** Drizzle with soy sauce, mirin, and sake. Toss to incorporate.\n6. **Garnish:** Serve hot, garnished with additional grated Parmigiano-Reggiano cheese and truffle oil. For an optional touch, add a few drops of dashi to enhance the umami flavor.\n\n**Tasting Notes:**\n\nThis innovative dish combines the rich and creamy flavors of Italian carbonara sauce with the savory and umami notes of Japanese cuisine. The truffle oil adds an aromatic and earthy element, while the soy sauce, mirin, and sake balance out the richness with a touch of sweetness and acidity. The dashi adds an umami boost, enhancing the overall depth of flavor."
}
role: "model"
]

The above output displays the chat history, capturing the user’s query and Gemini Pro’s response. This demonstrates how chat history provides a coherent and continuous conversation flow, essential for multi-turn dialogues.

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Multi-Turn Chat: Generating Code and Unit Tests

code_chat = model.start_chat()

response = code_chat.send_message(
    "Write a function in Python that calculates the Fibonacci sequence up to a given number n." 
    "The function should return a list of the sequence."
)

to_markdown(response.text)

In this block, a new chat session is initiated specifically for generating Python code. The user or we request Gemini Pro to write a function that calculates the Fibonacci sequence up to a given number. This showcases the model’s ability to switch contexts and generate technical content.

def fibonacci(n):
    """Calculates the Fibonacci sequence up to a given number n.

    Args:
        n: The number up to which the Fibonacci sequence should be calculated.

    Returns:
        A list of the Fibonacci sequence up to n.
    """

    # Initialize the Fibonacci sequence with the first two numbers.
    fib_sequence = [0, 1]

    # Iterate over the numbers from 2 to n.
    for i in range(2, n):
        # Calculate the next number in the Fibonacci sequence.
        next_number = fib_sequence[i - 1] + fib_sequence[i - 2]

        # Add the next number to the Fibonacci sequence.
        fib_sequence.append(next_number)

    # Return the Fibonacci sequence.
    return fib_sequence

Gemini Pro generates a Python function for calculating the Fibonacci sequence, demonstrating its capability to understand and respond to coding-related requests. The detailed code includes comments, further illustrating the model’s ability to produce clear and functional code snippets.

response = code_chat.send_message("Could you write a unit test for the generated fibonacci function?")

to_markdown(response.text)

Following the function generation, we ask Gemini Pro to write a unit test for the Fibonacci function. This request not only progresses the conversation toward software quality assurance but also serves as a key test of the chat model’s multi-turn capability.

By asking for unit tests after the initial function creation, we effectively evaluate Gemini Pro’s ability to maintain context and coherence over a series of exchanges. This demonstrates a crucial step in software development, ensuring the code functions as intended, while simultaneously testing the model’s capacity to engage in a complex, multi-step dialogue that reflects a real-world development workflow.

import unittest

class FibonacciTest(unittest.TestCase):

    def test_fibonacci(self):
        self.assertEqual(fibonacci(0), [0])
        self.assertEqual(fibonacci(1), [0, 1])
        self.assertEqual(fibonacci(2), [0, 1, 1])
        self.assertEqual(fibonacci(3), [0, 1, 1, 2])
        self.assertEqual(fibonacci(4), [0, 1, 1, 2, 3])
        self.assertEqual(fibonacci(5), [0, 1, 1, 2, 3, 5])
        self.assertEqual(fibonacci(6), [0, 1, 1, 2, 3, 5, 8])
        self.assertEqual(fibonacci(7), [0, 1, 1, 2, 3, 5, 8, 13])
        self.assertEqual(fibonacci(8), [0, 1, 1, 2, 3, 5, 8, 13, 21])
        self.assertEqual(fibonacci(9), [0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
        self.assertEqual(fibonacci(10), [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55])

The model responds with a Python unit test class for the Fibonacci function. This includes multiple test cases, confirming the model’s proficiency in not only generating initial code but also in contributing to software quality assurance through unit testing.

These sections together showcase Gemini Pro’s versatile capabilities in handling both creative and technical requests within a continuous, multi-turn chat session. The ability to maintain context through chat history enables a seamless transition between diverse topics, from culinary innovation to software development.

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Enhancing Conversations with Contextual Chat History

This section dives into the implementation of chat history in a conversational AI context, utilizing Python and the capabilities of a generative language model. By incorporating a predefined chat history, we simulate a richer, context-aware conversation between a user and the AI model. This technique enhances the model’s ability to provide relevant and informed responses based on the ongoing dialogue.

chat_history = model.start_chat(
    history=[
        glm.Content(
            role="user",
            parts=[
                glm.Part(text=
                    """
    My name is Alex. You are my travel guide. I'm passionate about exploring ancient civilizations and their landmarks.
    What destinations do you recommend?
    """
                )
            ],
        ),
        glm.Content(role="model", parts=[glm.Part(text="I recommend visiting the Pyramids of Giza in Egypt and Machu Picchu in Peru for a start.")]),
        glm.Content(role="user", parts=[glm.Part(text="Tell me something interesting about Machu Picchu.")]),
        glm.Content(role="model", parts=[glm.Part(text="Machu Picchu is often referred to as the 'Lost City of the Incas' and is a UNESCO World Heritage Site.")]),
    ]
)

In the above Python code block, we initialize a chat session with historical context. This is achieved by creating instances of glm.Content for both the user and the model, detailing an exchange about exploring ancient civilizations. The user, named Alex, asks for travel recommendations, and the model suggests visiting the Pyramids of Giza and Machu Picchu, followed by a request for interesting facts about Machu Picchu. This sets up a scenario where the model acts as a travel guide, offering personalized advice based on Alex’s interests.

Incorporating chat history into a session, as illustrated in the provided code snippet, preloads the conversation with essential context, setting the stage for the interaction. This inclusion of alternating messages between the user and model establishes a conversational backdrop, enabling the model to produce responses that are informed and relevant. This approach not only primes the model with the necessary context for a seamless dialogue flow but also mimics a memory function, allowing the model to reference past exchanges, user preferences, and the evolving nature of the conversation.

The use of the argument history captures the essence of this feature by creating a narrative of prior interactions, giving the model a semblance of continuity and awareness of the conversation’s trajectory. This simulated memory aids in crafting responses that are not just relevant but also coherent with the dialogue’s established themes, enhancing the natural flow of the chat as though it has been progressing over time, rather than initiating anew with each message. Therefore, the history argument is crucial for enriching the conversation, guiding the model’s understanding and responses based on the cumulative context of past exchanges.

response = chat_history.send_message("Are there any specific festivals or events related to ancient civilizations happening soon?")
to_markdown(response.text)

Following the established history, we send a new message inquiring about specific festivals or events related to ancient civilizations happening soon. This demonstrates how the model can continue the conversation by accessing and utilizing the provided chat history to generate a coherent and contextually appropriate response.

Yes, the Inti Raymi festival in Peru is a popular festival that takes place in June and celebrates the winter solstice and the Inca culture.

The model’s response reveals that the Inti Raymi festival, which honors the winter solstice and Inca culture in Peru, takes place in June. This demonstrates the model’s capacity to access and deliver precise information pertinent to the user’s request, given that the festival represents ancient civilization, leveraging the established conversational context.

response = chat_history.send_message("Can you tell me the specific date when the Inti Raymi Festival in Peru is celebrated??")
to_markdown(response.text)

In a subsequent Python block, a more precise question is posed about the specific date of the Inti Raymi Festival. This illustrates refining the conversation’s focus and seeking more detailed information following the initial response.

The Inti Raymi Festival in Peru is celebrated on June 24th each year.

The model clarifies that the Inti Raymi Festival is celebrated on June 24th each year, providing a direct answer to the refined question. This demonstrates the model’s capability to understand and respond to follow-up inquiries for additional details, further enriching the conversational experience.

Through these blocks, we exemplify a dynamic and interactive dialogue with the AI model, highlighting the use of chat history to create a more engaging and informative exchange tailored to the user’s interests and queries.

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Conversational Debugging with Gemini Pro: Refining Image Classification Code

In this section, we continue our journey with Gemini Pro, delving deeper into its capabilities by testing it on a task we’ve previously tackled: generating image classification code. However, this time, we leverage Gemini Pro’s chat feedback mechanism to engage in a dialogue, pointing out inaccuracies or errors and requesting corrections. This interactive process not only showcases the model’s ability to participate in conversational debugging but also tests its capacity to understand and rectify coding errors based on feedback.

model = genai.GenerativeModel('gemini-pro')
chat = model.start_chat(history=[])

Here, we initialize a chat session with Gemini Pro, setting the stage for our conversational debugging exercise. This initial step prepares the model for the interactive session, where we aim to generate and refine the image classification code.

response = chat.send_message("Write a multiclass image classification code in pytorch framework using a public dataset, I would be training and testing the image classification code in Google colab")
to_markdown(response.text)

We prompt Gemini Pro to generate PyTorch code for multiclass image classification, specifying the use of a public dataset and Google Colab as the environment for training and testing. This sets a clear task for the model, aiming to generate usable code for a specific framework and dataset.

# Import necessary libraries
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# Define the device to use for training and testing
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the public image dataset
train_dataset = torchvision.datasets.ImageFolder(root='path/to/train_directory', transform=torchvision.transforms.ToTensor())
test_dataset = torchvision.datasets.ImageFolder(root='path/to/test_directory', transform=torchvision.transforms.ToTensor())

# Create data loaders for training and testing
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# Define the image classification model architecture
class ImageClassifier(nn.Module):
    def __init__(self):
        super(ImageClassifier, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(128 * 7 * 7, 1024)
        self.fc2 = nn.Linear(1024, 3) # Adjust this to match the number of classes in your dataset

    def forward(self, x):
        x = self.conv1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = x.view(-1, 128 * 7 * 7)
        x = self.fc1(x)
        x = self.fc2(x)
        return x

# Initialize the model
model = ImageClassifier().to(device)

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# Train the model
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        # Get the inputs
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # Zero the parameter gradients
        optimizer.zero_grad()

        # Forward pass
        outputs = model(inputs)

        # Compute the loss
        loss = criterion(outputs, labels)

        # Backward pass
        loss.backward()

        # Update the parameters
        optimizer.step()

        # Print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

# Test the model
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))
To train and test the image classification code in Google Colab:
    Create a new Colab notebook.
    Import the necessary libraries.
    Load the public image dataset.
    Create data loaders for training and testing.
    Define the image classification model architecture.
    Initialize the model.
    Define the loss function and optimizer.
    Train the model.
    Test the model.
You can find the public image dataset and the necessary libraries in the resources section of the Colab notebook.

The response from the model is a comprehensive PyTorch script tailored for image classification, covering crucial phases from library importation and model architecture definition to the outlining of training and testing routines. Despite its thoroughness, certain issues merit attention for refinement: the output layer’s number of classes is fixed, indicating a lack of adaptability for datasets varying in class count; activation functions are notably absent, stunting the network’s learning capability; and execution of the provided code reveals runtime and value errors, possibly hinting at deeper architectural mismatches. These elements could significantly enhance the code’s versatility and operational smoothness across diverse datasets.

response = chat.send_message("Can you use a public dataset like cifar-10 or mnist?", stream=True)

Following the initial code generation, we inquire about adapting the code for well-known public datasets like CIFAR-10 (Canadian Institute For Advanced Research) or MNIST (Modified National Institute of Standards and Technology). This question tests the model’s ability to modify the generated code to accommodate different datasets, showcasing its adaptability.

Yes, you can use a public dataset like CIFAR-10 or MN
IST in the provided code. Here's how you can do it:

**For CIFAR-10:**

1. Import the `torchvision
.datasets` module.
2. Load the CIFAR-10 dataset using the following code:

```python
train_dataset = torchvision.datasets.CIFAR10(root='path/to/cifar10_train', train=True, download=True, transform=torchvision.
transforms.ToTensor())
test_dataset = torchvision.datasets.CIFAR10(root='path/to/cifar10_test', train=False, download=True, transform=torchvision.transforms.ToTensor())
```

3. Create data loaders for training and testing as usual.

**For MNIST:**

1. Import the `torchvision.datasets` module.
2. Load the MNIST dataset using the following code:

```python
train_dataset = torchvision.datasets.MNIST(root='path/to/mnist_train', train=True, download=True,
 transform=torchvision.transforms.ToTensor())
test_dataset = torchvision.datasets.MNIST(root='path/to/mnist_test', train=False, download=True, transform=torchvision.transforms.ToTensor())
```

3. Create data loaders for training and testing as usual.

Once you have loaded the dataset, you can follow the rest of the code as provided earlier. You may need to adjust the number of classes in the model's fully connected layer to match the number of classes in the dataset you are using. For example, for CIFAR-10, you would change the number of classes to 10, and for MNIST, you would change it to 10 as well.

Here is the modified code for the model initialization:

```python
class ImageClassifier(nn.Module):
    def __init__(self):
        super(ImageClassifier, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2
 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(128 * 7 * 7, 1024)
        self.fc2 = nn.Linear(1024, 10) # Adjust this to match the number of classes in your dataset

    def forward(self, x):
        x = self.conv1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = x.view(-1, 128 * 7 * 7)
        x = self.fc1(x)
        x = self.fc2(x)
        return x
```

With these changes, you can use the code to train and test the image classification model on either the CIFAR-10 or MNIST dataset.

The model confirms the feasibility of using CIFAR-10 or MNIST and guides the loading of these datasets. This demonstrates Gemini Pro’s responsiveness to feedback and its capacity to offer practical solutions for refining the code based on user requirements.

response = chat.send_message("Okay, how about pass a root as ./data to cifar10?", stream=True)

for chunk in response:
  print(chunk.text)

We further refine our request by specifying a data path for the CIFAR-10 dataset. This tests the model’s ability to incorporate specific user inputs into the code, enhancing its relevance and usability.

To pass a root of `./data` to CIFAR-10 in
 the provided code, you can use the following code:

```python
train_dataset = torchvision.datasets.CIFAR10(root='./data
', train=True, download=True, transform=torchvision.transforms.ToTensor())
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=torchvision.transforms.ToTensor())
```

This will download the CIFAR-
10 dataset to the `./data` directory and then load it into the training and testing datasets.

Here is the complete modified code:

```python
# Import necessary libraries
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# Define the device to use for training and testing
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the CIFAR-10 dataset
train_dataset = torchvision.datasets.CIFAR10(root='./data',
 train=True, download=True, transform=torchvision.transforms.ToTensor())
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=torchvision.transforms.ToTensor())

# Create data loaders for training and testing
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# Define the image classification model architecture
class ImageClassifier(nn.Module):
    def __init__(self):
        super(ImageClassifier, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=
2)
        self.fc1 = nn.Linear(128 * 7 * 7, 1024)
        self.fc2 = nn.Linear(1024, 10) # Adjust this to match the number of classes in your dataset

    def forward(self, x):
        x = self.conv1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = x.view(-1, 128 * 7 * 7)
        x = self.fc1(x)
        x = self.fc2(x)
        return x

# Initialize the model
model = ImageClassifier().to(device)

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# Train the model
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        # Get the inputs
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # Zero the parameter gradients
        optimizer.zero_grad()

        # Forward pass
        outputs = model(inputs)

        # Compute the loss
        loss = criterion(outputs, labels)

        # Backward pass
        loss.backward()

        # Update the parameters
        optimizer.step()

        # Print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

# Test the model
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model
(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))
```

Now, when you run this code, it will download the CIFAR-10 dataset to the `./data` directory and then train and test the image classification model on the dataset.

Gemini Pro successfully adjusts the code to use the specified ./data path for downloading and loading the CIFAR-10 dataset. This adjustment showcases the model’s flexibility and its capacity to tailor the code based on detailed user specifications.

response = chat.send_message("the imageclassifier class does not use any activation function which is essential for adding non-linearity", stream=True)

for chunk in response:
  print(chunk.text)

In this step, we highlight a critical issue in the generated code: the absence of activation functions in the ImageClassifier model, which are crucial for learning complex patterns. This feedback is aimed at improving the model’s performance by introducing non-linearity.

You're right, the `ImageClassifier` class in the provided code does
 not use any activation function. Activation functions are essential for adding non-linearity to the model, which is necessary for it to learn complex patterns in the data
.

To add non-linearity to the model, you can use activation functions such as ReLU, LeakyReLU, or Sigmoid. Here is the modified code with a ReLU activation function added after each convolutional layer and the first fully connected layer:

```python
class ImageClassifier(nn.Module
):
    def __init__(self):
        super(ImageClassifier, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.relu
2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(128 * 7 * 7, 1024)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(1024, 10) # Adjust this to match the number of classes in your dataset

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = x.view(-1, 128 * 7 * 7)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        return x
```

With this change, the model will now use the ReLU activation function after each convolutional layer and the
 first fully connected layer. This will add non-linearity to the model and help it learn more complex patterns in the data.

Remember to also import the necessary activation function at the beginning of the code:

```python
import torch.nn.functional as F
```

And use `F.relu()` for the activation function in the model:

```python
self.relu1 = F.relu()
self.relu2 = F.relu()
self.relu3 = F.relu()
```

Now, when you train the model, it should perform better due to the addition of the activation function.

Acknowledging the feedback, Gemini Pro revises the model to include ReLU activation functions at appropriate layers. This correction not only addresses the identified error but also exemplifies the model’s ability to understand and implement specific coding recommendations, thereby improving the overall code quality.

---

Unveiling the Limits: Advanced Error Resolution in Conversational Debugging

Through this interactive debugging session, we’ve engaged Gemini Pro in a multi-turn chat that generates code, identifies potential issues, and refines the output based on conversational feedback. This process not only reinforces the model’s utility as a coding assistant but also highlights its potential in conversational AI applications where understanding, adapting to, and acting on user feedback is critical.

These discussions we’ve detailed represent just a fraction of the extensive dialogue we engaged in with Gemini Pro. Following our conversation about integrating activation functions, we encountered further challenges, such as runtime and value errors during the model’s training phase. Specifically, we faced a value error indicating a mismatch between the expected input batch size and the target batch size, a critical issue that arose during the loss computation process. This suggested a deeper problem, possibly rooted in the architecture’s design or the data preprocessing steps.

We promptly reported these issues back to Gemini Pro, describing the errors encountered during code execution and seeking guidance on resolution. Although Gemini Pro attempted to address these concerns, the dialogue unfolded over three to four additional exchanges where it became evident that resolving these deep-level errors was a complex task. Despite the model’s adeptness at adjusting simpler aspects of the code (e.g., adding missing activation functions or specifying data paths), it struggled to rectify the more intricate issues that emerged during the computational processes, particularly those affecting the loss calculation.

Ultimately, while Gemini Pro proved to be an invaluable resource for identifying and correcting certain errors through conversational feedback, it faced limitations when addressing more complex problems that required a nuanced understanding of the neural network architecture and its interaction with the data. This experience highlighted the potential and boundaries of using conversational AI for debugging and refining machine learning code, underscoring the importance of human oversight in navigating the intricacies of model development and error resolution.

---

Accessing Full Dialogue and Code

For those keen to explore the entirety of our dialogue with Gemini Pro, including the attempts to navigate through complex debugging scenarios, the full exchange is documented in our accompanying Colab notebook. This resource, integral to our tutorial, is available for direct access and download, offering a comprehensive view of conversational debugging in action and serving as a practical guide through the nuanced interactions between AI-driven coding assistance and the multifaceted challenges of machine learning model development.

---

---

Summary

In this Part 4 of our Gemini Pro series, we embark on an explorative journey into the conversational AI capabilities of Gemini Pro, particularly focusing on its utility in the realm of coding and debugging. The tutorial begins by introducing readers to the initial setup and configuration of Gemini Pro for chat-based interactions, setting the stage for a deep dive into its chat functionalities. Through a structured walkthrough, we explore various aspects of interacting with Gemini Pro, including setting up the development environment, listing available models, and enhancing code presentation with Markdown.

A significant portion of the tutorial is dedicated to engaging Gemini Pro in a multi-turn chat, demonstrating the model’s ability to generate code snippets and unit tests. This process not only highlights the practical applications of Gemini Pro but also puts its conversational debugging prowess to the test. We encounter a series of challenges, from basic fixes like adding missing activation functions to more complex issues related to runtime and value errors during code execution. These encounters serve as real-world examples of Gemini Pro’s capabilities and limitations in addressing deep-level errors within the code.

The tutorial culminates in a detailed examination of conversational debugging with Gemini Pro, showcasing how it facilitates the refinement of image classification code through dialogue. Despite its successes in certain areas, Gemini Pro’s struggles with more intricate debugging tasks reveal critical insights into the limitations of current AI technologies in understanding and rectifying complex coding errors without human intervention.

This exploration not only provides a comprehensive overview of Gemini Pro’s chat and coding functionalities but also sheds light on the potential and boundaries of using conversational AI for code generation and debugging. It underscores the necessity for ongoing human oversight in the AI-assisted coding process, particularly when navigating the nuanced challenges of software development.

---

Citation Information

Sharma, A. “Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue,” PyImageSearch, P. Chugh, A. R. Gosthipaty, S. Huot, K. Kidriavsteva, and R. Raha, eds., 2024, https://pyimg.co/onf1q

@incollection{Sharma_2024_ConversingGeminiPro,
  author = {Aditya Sharma},
  title = {Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue},
  booktitle = {PyImageSearch},
  editor = {Puneet Chugh and Aritra Roy Gosthipaty and Susan Huot and Kseniia Kidriavsteva and Ritwik Raha},
  year = {2024},
  url = {https://pyimg.co/onf1q},
}

---

---

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