app.py

import os
import pandas as pd
import gradio as gr
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_groq import ChatGroq
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain_core.documents import Document

# Hardcoded Groq API key
GROK_API_KEY = "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"

# Initialize LLM (Grok)
def initialize_llm():
    return ChatGroq(
        temperature=0.7,
        groq_api_key=GROK_API_KEY,
        model_name="llama-3.3-70b-versatile"
    )

llm = initialize_llm()

# Load and prepare the CSV dataset, then create or load FAISS index
def create_or_load_faiss_index():
    index_path = "faiss_index"
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    
    if os.path.exists(index_path):
        vector_db = FAISS.load_local(index_path, embeddings, allow_dangerous_deserialization=True)
    else:
        csv_path = "A_Z_medicines_dataset_of_India.csv"
        if not os.path.exists(csv_path):
            raise FileNotFoundError(f"Dataset not found at: {csv_path}")
        
        df = pd.read_csv(csv_path)
        documents = [
            Document(
                page_content=row["name"],
                metadata={"short_composition1": row["short_composition1"]}
            )
            for _, row in df.iterrows()
            if pd.notna(row["name"]) and pd.notna(row["short_composition1"])
        ]
        
        vector_db = FAISS.from_documents(documents, embeddings)
        vector_db.save_local(index_path)
    
    return vector_db

vector_db = create_or_load_faiss_index()

# Set up QA chain
retriever = vector_db.as_retriever(search_kwargs={"k": 1})
prompt_template = """You are DrugScan, a medical assistant that explains drug compositions. Provide a detailed explanation of the drug based on its active ingredient and dosage, including its uses, mechanism of action, potential side effects, and any relevant precautions. Be empathetic and clear in your response.

Drug Composition: {context}
User Query: {question}
DrugScan: """
PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    chain_type_kwargs={"prompt": PROMPT},
    return_source_documents=True
)

# Suggested drugs
suggested_drugs = [
    "Azirox",
    "Augmentin",
    "Ascoril LS",
    "Allepra 120",
    "Amoxycillin",
]

# Function to handle drug query
def query_drug(drug_name, chat_history):
    if not drug_name.strip():
        chat_history.append({"role": "assistant", "content": "Please enter a drug name."})
        return chat_history
    
    try:
        result = qa_chain.invoke({"query": drug_name})
        if not result["source_documents"]:
            response = "Drug not found in the dataset. Please try another drug name."
        else:
            composition = result["source_documents"][0].metadata["short_composition1"]
            response = f"{result['result']}\n\n**Drug Composition:** {composition}"
    except Exception as e:
        error_msg = str(e)
        if "rate limit" in error_msg.lower() or "quota" in error_msg.lower():
            response = "Error: Rate limit or quota exceeded for the Groq API. Please try again later."
        elif "connection" in error_msg.lower() or "network" in error_msg.lower():
            response = "Error: Network issue while connecting to the Groq API. Please check your internet connection."
        else:
            response = f"Error: An unexpected error occurred: {error_msg}"
    
    chat_history.append({"role": "user", "content": drug_name})
    chat_history.append({"role": "assistant", "content": response})
    return chat_history

# Function to handle suggested drug buttons
def query_suggested_drug(drug_name, chat_history):
    return query_drug(drug_name, chat_history)

# Gradio Interface
with gr.Blocks(title="DrugScan") as demo:
    gr.Markdown("# DrugScan")
    gr.Markdown("Enter the name of a drug to learn about its active ingredients, uses, mechanism of action, side effects, and more.")
    
    # Display logo
    logo_url = "https://i.postimg.cc/gJ9Z0RGS/bc20af1b-8ee6-4e1c-8748-eba44e2780c1-removalai-preview.png"
    gr.Image(logo_url, width=150)
    
    # Chat interface
    chatbot = gr.Chatbot(label="Results", type="messages")
    drug_input = gr.Textbox(placeholder="Enter a drug name (e.g., 'Azirox')", label="Drug Name")
    
    # Search button
    search_btn = gr.Button("Search")
    search_btn.click(
        fn=query_drug,
        inputs=[drug_input, chatbot],
        outputs=chatbot
    )
    
    # Suggested drugs buttons
    gr.Markdown("### Try These Drugs")
    with gr.Row():
        for drug in suggested_drugs:
            btn = gr.Button(drug)
            btn.click(
                fn=query_suggested_drug,
                inputs=[gr.State(value=drug), chatbot],
                outputs=chatbot
            )
    
    # Disclaimer
    gr.Markdown("### Important Disclaimer")
    gr.Markdown(
        "DrugScan provides explanations of drug compositions based on available data. It is not a substitute for professional medical advice or diagnosis. Always consult a qualified healthcare provider for personal health concerns."
    )

# Launch the app
demo.launch()