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app.py

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-import streamlit as st
-
-st.title("Medical RAG and Reasoning App")
-st.write("This app demonstrates Retrieval-Augmented Generation (RAG) for medical question answering.")
-
-#!/usr/bin/env python
-# coding: utf-8
-
-# # HuatuoGPT-o1 Medical RAG and Reasoning
-# 
-# _Authored by: [Alan Ponnachan](https://huggingface.co/AlanPonnachan)_
-# 
-# This notebook demonstrates an end-to-end example of using HuatuoGPT-o1 for medical question answering with Retrieval-Augmented Generation (RAG) and reasoning. We'll leverage the HuatuoGPT-o1 model, a medical Large Language Model (LLM) designed for advanced medical reasoning, to provide detailed and well-structured answers to medical queries.
-# 
-# ## Introduction
-# 
-# HuatuoGPT-o1 is a medical LLM that excels at identifying mistakes, exploring alternative strategies, and refining its answers. It utilizes verifiable medical problems and a specialized medical verifier to enhance its reasoning capabilities. This notebook showcases how to use HuatuoGPT-o1 in a RAG setting, where we retrieve relevant information from a medical knowledge base and then use the model to generate a reasoned response.
-
-# ##  Notebook Setup
-# 
-# 
-# **Important:** Before running the code, ensure you are using a GPU runtime for faster performance. Go to **"Runtime" -> "Change runtime type"** and select **"GPU"** under "Hardware accelerator."
-# 
-# Let's start by installing the necessary libraries.
-
-# In[1]:
-
-
-get_ipython().system('pip install transformers datasets sentence-transformers scikit-learn --upgrade -q')
-
-
-# ##  Load the Dataset
-# 
-# We'll use the **"ChatDoctor-HealthCareMagic-100k"** dataset from the Hugging Face Datasets library. This dataset contains 100,000 real-world patient-doctor interactions, providing a rich knowledge base for our RAG system.
-
-# In[2]:
-
-
-from datasets import load_dataset
-
-dataset = load_dataset("lavita/ChatDoctor-HealthCareMagic-100k")
-
-
-# ## Step 3: Initialize the Models
-# 
-# We need to initialize two models:
-# 
-# 1. **HuatuoGPT-o1**: The medical LLM for generating responses.
-# 2. **Sentence Transformer**: An embedding model for creating vector representations of text, which we'll use for retrieval.
-
-# In[3]:
-
-
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from sentence_transformers import SentenceTransformer
-
-# Initialize HuatuoGPT-o1
-model_name = "FreedomIntelligence/HuatuoGPT-o1-7B"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name, torch_dtype="auto", device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Initialize Sentence Transformer
-embed_model = SentenceTransformer("all-MiniLM-L6-v2")
-
-
-# ## Prepare the Knowledge Base
-# 
-# We'll create a knowledge base by generating embeddings for the combined question-answer pairs from the dataset.
-
-# In[4]:
-
-
-import pandas as pd
-import numpy as np
-
-# Convert dataset to DataFrame
-df = pd.DataFrame(dataset["train"])
-
-# Combine question and answer for context
-df["combined"] = df["input"] + " " + df["output"]
-
-# Generate embeddings
-st.write("Generating embeddings for the knowledge base...")
-embeddings = embed_model.encode(
-    df["combined"].tolist(), show_progress_bar=True, batch_size=128
-)
-st.write("Embeddings generated!")
-
-
-# ## Implement Retrieval
-# 
-# This function retrieves the `k` most relevant contexts to a given query using cosine similarity.
-
-# In[5]:
-
-
-from sklearn.metrics.pairwise import cosine_similarity
-
-def retrieve_relevant_contexts(query: str, k: int = 3) -> list:
-    """
-    Retrieves the k most relevant contexts to a given query.
-
-    Args:
-        query (str): The user's medical query.
-        k (int): The number of relevant contexts to retrieve.
-
-    Returns:
-        list: A list of dictionaries, each containing a relevant context.
-    """
-    # Generate query embedding
-    query_embedding = embed_model.encode([query])[0]
-
-    # Calculate similarities
-    similarities = cosine_similarity([query_embedding], embeddings)[0]
-
-    # Get top k similar contexts
-    top_k_indices = np.argsort(similarities)[-k:][::-1]
-
-    contexts = []
-    for idx in top_k_indices:
-        contexts.append(
-            {
-                "question": df.iloc[idx]["input"],
-                "answer": df.iloc[idx]["output"],
-                "similarity": similarities[idx],
-            }
-        )
-
-    return contexts
-
-
-# ## Implement Response Generation
-# 
-# This function generates a detailed response using the retrieved contexts.
-
-# In[6]:
-
-
-def generate_structured_response(query: str, contexts: list) -> str:
-    """
-    Generates a detailed response using the retrieved contexts.
-
-    Args:
-        query (str): The user's medical query.
-        contexts (list): A list of relevant contexts.
-
-    Returns:
-        str: The generated response.
-    """
-    # Prepare prompt with retrieved contexts
-    context_prompt = "\n".join(
-        [
-            f"Reference {i+1}:"
-            f"\nQuestion: {ctx['question']}"
-            f"\nAnswer: {ctx['answer']}"
-            for i, ctx in enumerate(contexts)
-        ]
-    )
-
-    prompt = f"""Based on the following references and your medical knowledge, provide a detailed response:
-
-References:
-{context_prompt}
-
-Question: {query}
-
-By considering:
-1. The key medical concepts in the question.
-2. How the reference cases relate to this question.
-3. What medical principles should be applied.
-4. Any potential complications or considerations.
-
-Give the final response:
-"""
-
-    # Generate response
-    messages = [{"role": "user", "content": prompt}]
-    inputs = tokenizer(
-        tokenizer.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
-        ),
-        return_tensors="pt",
-    ).to(model.device)
-
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=1024,
-        temperature=0.7,
-        num_beams=1,
-        do_sample=True,
-    )
-
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    # Extract the final response portion
-    final_response = response.split("Give the final response:\n")[-1]
-
-    return final_response
-
-
-# ## Putting It All Together
-# 
-# Let's define a function to process a query end-to-end and then use it with an example.
-
-# In[7]:
-
-
-def process_query(query: str, k: int = 3) -> tuple:
-    """
-    Processes a medical query end-to-end.
-
-    Args:
-        query (str): The user's medical query.
-        k (int): The number of relevant contexts to retrieve.
-
-    Returns:
-        tuple: The generated response and the retrieved contexts.
-    """
-    contexts = retrieve_relevant_contexts(query, k)
-    response = generate_structured_response(query, contexts)
-    return response, contexts
-
-# Example query
-query = "I've been experiencing persistent headaches and dizziness for the past week. What could be the cause?"
-
-# Process query
-response, contexts = process_query(query)
-
-# Print results
-st.write("\nQuery:", query)
-st.write("\nRelevant Contexts:")
-for i, ctx in enumerate(contexts, 1):
-    st.write(f"\nReference {i} (Similarity: {ctx['similarity']:.3f}):")
-    st.write(f"Q: {ctx['question']}")
-    st.write(f"A: {ctx['answer']}")
-
-st.write("\nGenerated Response:")
-st.write(response)
-
-
-# ## Conclusion
-# 
-# This notebook demonstrates a practical application of HuatuoGPT-o1 for medical question answering using RAG and reasoning. By combining retrieval from a relevant knowledge base with the advanced reasoning capabilities of HuatuoGPT-o1, we can build a system that provides detailed and well-structured answers to complex medical queries.
-# 
-# You can further enhance this system by:
-# 
-# *   Experimenting with different values of `k` (number of retrieved contexts).
-# *   Fine-tuning HuatuoGPT-o1 on a specific medical domain.
-# *   Evaluating the system's performance using medical benchmarks.
-# *   Adding a user interface for easier interaction.
-# *   Improving upon existing code by handling edge cases.
-# 
-# Feel free to adapt and expand upon this example to create even more powerful and helpful medical AI applications!