Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/14vp_MnMkFmCm_l4xkpQa0lIN07A4Z1RQ
+"""
+
+
+
+"""# Task
+Develop a Gradio application with a chatbot tab that uses a conversational model from Hugging Face to interact with users.
+
+## Set up the environment
+
+### Subtask:
+Install the necessary libraries, such as `transformers` and `gradio`.
+
+**Reasoning**:
+The subtask requires installing the `transformers` and `gradio` libraries. I will use pip to install both libraries in a single code block.
+"""
+
+!pip install transformers gradio
+
+"""## Load the model and tokenizer
+
+### Subtask:
+Choose a suitable conversational model from Hugging Face and load it along with its tokenizer.
+
+**Reasoning**:
+Import the necessary classes and load the chosen conversational model and its tokenizer from Hugging Face.
+"""
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "microsoft/DialoGPT-medium"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+"""## Define the chat function
+
+### Subtask:
+Create a Python function that takes user input, processes it using the loaded model, and returns the chatbot's response.
+
+**Reasoning**:
+Define a function to handle user input, tokenize it, generate a response using the loaded model, and decode the response.
+"""
+
+def chat_with_bot(user_input, history):
+    # The history from Gradio Chatbot is a list of [user_message, bot_message] pairs.
+    # We need to reconstruct the full conversation history.
+    full_conversation = ""
+    for user_msg, bot_msg in history:
+        full_conversation += user_msg + tokenizer.eos_token
+        full_conversation += bot_msg + tokenizer.eos_token
+
+    # Add the current user input to the conversation
+    full_conversation += user_input + tokenizer.eos_token
+
+    # Encode the full conversation
+    input_ids = tokenizer.encode(full_conversation, return_tensors="pt")
+
+    # Generate a response from the model
+    # Pass the entire chat history tensor to the model for generation
+    output_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+    # Decode the model's response (excluding the input part)
+    response = tokenizer.decode(output_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+    return response
+
+"""## Build the gradio interface
+
+### Subtask:
+Use Gradio to create a conversational interface that connects the chat function to the user interface.
+
+**Reasoning**:
+Create a Gradio interface that connects the chat function to the user interface as described in the instructions.
+"""
+
+import gradio as gr
+
+iface = gr.ChatInterface(fn=chat_with_bot,
+                         title="Hugging Face Conversational Chatbot")
+
+"""**Reasoning**:
+Launch the Gradio interface to make it available for interaction.
+
+
+"""
+
+iface.launch()
+
+"""## Summary:
+
+### Data Analysis Key Findings
+
+*   The task successfully installed the necessary libraries (`transformers` and `gradio`).
+*   A conversational model (`microsoft/DialoGPT-medium`) and its tokenizer were successfully loaded from Hugging Face.
+*   A Python function `chat_with_bot` was created to process user input using the loaded model and return a response.
+*   A Gradio interface was built and launched, connecting the `chat_with_bot` function to a user-friendly interface with input and output textboxes.
+
+### Insights or Next Steps
+
+*   The current implementation uses a fixed model. Future work could explore allowing users to choose different conversational models.
+*   The chat function does not currently maintain conversation history, which limits the chatbot's ability to have coherent multi-turn conversations. Adding memory to the chat function would be a valuable improvement.
+
+"""