app.py

# Import required libraries
import os
import pandas as pd
import streamlit as st
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import requests
import json

# Configure Hugging Face API token securely (ensure it's set in environment variables)
api_key = os.getenv("HF_API_KEY")

# Load the CSV dataset (place the CSV in the same directory as app.py in Hugging Face Spaces)
try:
    data = pd.read_csv('genetic-Final.csv')  # Ensure the dataset filename is correct
except FileNotFoundError:
    st.error("Dataset file not found. Please upload it to this directory.")

# Initialize Sentence Transformer model for RAG-based retrieval
retriever_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Preprocess the dataset by creating a combined description column
if 'combined_description' not in data.columns:
    data['combined_description'] = (
        data['Symptoms'].fillna('') + " " +
        data['Severity Level'].fillna('') + " " +
        data['Risk Assessment'].fillna('') + " " +
        data['Treatment Options'].fillna('') + " " +
        data['Suggested Medical Tests'].fillna('') + " " +
        data['Minimum Values for Medical Tests'].fillna('') + " " +
        data['Emergency Treatment'].fillna('')
    )

# Define weights for each column based on importance
column_weights = {
    'Symptoms': 0.4,
    'Severity Level': 0.2,
    'Risk Assessment': 0.1,
    'Treatment Options': 0.15,
    'Suggested Medical Tests': 0.05,
    'Minimum Values for Medical Tests': 0.05,
    'Emergency Treatment': 0.05
}

# Precompute embeddings for each weighted column
for col in column_weights.keys():
    if f"{col}_embeddings" not in data.columns:
        data[f"{col}_embeddings"] = data[col].fillna("").apply(lambda x: retriever_model.encode(x).tolist())

# Function to retrieve relevant information with weighted scoring
def get_weighted_relevant_info(query, top_k=3):
    query_embedding = retriever_model.encode(query)
    weighted_similarities = []
    for idx, row in data.iterrows():
        weighted_score = 0
        for col, weight in column_weights.items():
            if row[f"{col}_embeddings"]:
                col_similarity = util.cos_sim(query_embedding, row[f"{col}_embeddings"])[0][0].item()
                weighted_score += col_similarity * weight
        weighted_similarities.append(weighted_score)
    
    top_indices = sorted(range(len(weighted_similarities)), key=lambda i: weighted_similarities[i], reverse=True)[:top_k]
    return data.iloc[top_indices]

# Generate embeddings for the combined description if not already done
if 'embeddings' not in data.columns:
    data['embeddings'] = data['combined_description'].apply(lambda x: retriever_model.encode(x).tolist() if x else [])

# Function to retrieve relevant information based on user query (non-weighted)
def get_relevant_info(query, top_k=3):
    query_embedding = retriever_model.encode(query)
    similarities = [util.cos_sim(query_embedding, doc_emb)[0][0].item() for doc_emb in data['embeddings']]
    top_indices = sorted(range(len(similarities)), key=lambda i: similarities[i], reverse=True)[:top_k]
    return data.iloc[top_indices]

# Enhanced response generation function with debugging
# Import required libraries
import os
import pandas as pd
import streamlit as st
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import requests
import json

# Configure Hugging Face API token securely (ensure it's set in environment variables)
api_key = os.getenv("HF_API_KEY")

# Load the CSV dataset (place the CSV in the same directory as app.py in Hugging Face Spaces)
try:
    data = pd.read_csv('genetic-Final.csv')  # Ensure the dataset filename is correct
except FileNotFoundError:
    st.error("Dataset file not found. Please upload it to this directory.")

# Initialize Sentence Transformer model for RAG-based retrieval
retriever_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Preprocess the dataset by creating a combined description column
if 'combined_description' not in data.columns:
    data['combined_description'] = (
        data['Symptoms'].fillna('') + " " +
        data['Severity Level'].fillna('') + " " +
        data['Risk Assessment'].fillna('') + " " +
        data['Treatment Options'].fillna('') + " " +
        data['Suggested Medical Tests'].fillna('') + " " +
        data['Minimum Values for Medical Tests'].fillna('') + " " +
        data['Emergency Treatment'].fillna('')
    )

# Define weights for each column based on importance
column_weights = {
    'Symptoms': 0.4,
    'Severity Level': 0.2,
    'Risk Assessment': 0.1,
    'Treatment Options': 0.15,
    'Suggested Medical Tests': 0.05,
    'Minimum Values for Medical Tests': 0.05,
    'Emergency Treatment': 0.05
}

# Precompute embeddings for each weighted column
for col in column_weights.keys():
    if f"{col}_embeddings" not in data.columns:
        data[f"{col}_embeddings"] = data[col].fillna("").apply(lambda x: retriever_model.encode(x).tolist())

# Function to retrieve relevant information with weighted scoring
def get_weighted_relevant_info(query, top_k=3):
    query_embedding = retriever_model.encode(query)
    weighted_similarities = []
    for idx, row in data.iterrows():
        weighted_score = 0
        for col, weight in column_weights.items():
            if row[f"{col}_embeddings"]:
                col_similarity = util.cos_sim(query_embedding, row[f"{col}_embeddings"])[0][0].item()
                weighted_score += col_similarity * weight
        weighted_similarities.append(weighted_score)
    
    top_indices = sorted(range(len(weighted_similarities)), key=lambda i: weighted_similarities[i], reverse=True)[:top_k]
    return data.iloc[top_indices]

# Generate embeddings for the combined description if not already done
if 'embeddings' not in data.columns:
    data['embeddings'] = data['combined_description'].apply(lambda x: retriever_model.encode(x).tolist() if x else [])

# Function to retrieve relevant information based on user query (non-weighted)
def get_relevant_info(query, top_k=3):
    query_embedding = retriever_model.encode(query)
    similarities = [util.cos_sim(query_embedding, doc_emb)[0][0].item() for doc_emb in data['embeddings']]
    top_indices = sorted(range(len(similarities)), key=lambda i: similarities[i], reverse=True)[:top_k]
    return data.iloc[top_indices]

# Enhanced response generation function with debugging
def generate_response(input_text, relevant_info):
    context = "\n".join(relevant_info['combined_description'].tolist())
    input_with_context = f"Context: {context}\n\nUser Query: {input_text}"
    
    api_url = "https://api-inference.huggingface.co/models/m42-health/Llama3-Med42-8B"
    headers = {"Authorization": f"Bearer {api_key}"}
    payload = {"inputs": input_with_context}

    try:
        response = requests.post(api_url, headers=headers, json=payload)
        st.write("API Raw Response:", response.text)  # Display raw response for debugging

        # Check response status
        if response.status_code != 200:
            return f"Error: API responded with status code {response.status_code}. Full response: {response.json()}"

        # Parse and validate response
        response_data = response.json()
        if isinstance(response_data, list) and "generated_text" in response_data[0]:
            return response_data[0]["generated_text"]
        else:
            return f"Unexpected response format from API. Full response: {response_data}"
    except Exception as e:
        return f"Error during API request: {e}"



# Streamlit UI for the Chatbot
def main():
    st.title("Medical Report and Analysis Chatbot")
    st.sidebar.header("Upload Medical Report or Enter Query")

    # Text input for user queries
    user_query = st.sidebar.text_input("Type your question or query")

    # File uploader for medical report
    uploaded_file = st.sidebar.file_uploader("Upload a medical report (optional)", type=["txt", "pdf", "csv"])

    # Process the query if provided
    if user_query:
        st.write("### FAQ and Responses:")

        # Retrieve relevant information from the dataset
        relevant_info = get_weighted_relevant_info(user_query)
        for i, row in relevant_info.iterrows():
            st.write(f"- {row['combined_description']}")

        # Generate a response from the model
        response = generate_response(user_query, relevant_info)
        st.write("#### Model's Response:")
        st.write(response)

    # Process the uploaded file (if any)
    if uploaded_file:
        st.write("### Uploaded Report Analysis:")
        report_text = "Extracted report content here"  # Placeholder for file processing
        st.write(report_text)

if __name__ == "__main__":
    main()