Create app.py

app.py

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+import json
+import os
+import gradio as gr
+from langchain_groq import ChatGroq
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader
+from transformers import pipeline
+import traceback
+
+# Load psychiatrist details from JSON file
+def load_psychiatrists_data():
+    try:
+        json_path = "psychiatrists_data.json"  # Adjusted for local use
+        with open(json_path, "r", encoding="utf-8") as file:
+            data = json.load(file)
+            return {key.strip().lower(): value for key, value in data.get("India", {}).items()}
+    except FileNotFoundError:
+        print("❌ Error: psychiatrists_data.json file not found.")
+        return {}
+
+doc_data = load_psychiatrists_data()
+
+# Initialize sentiment analysis model
+sentiment_classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+# Initialize LLM
+def initialize_llm():
+    return ChatGroq(
+        temperature=0,
+        groq_api_key=os.getenv("GROQ_API_KEY"),  # Use environment variable for security
+        model_name="llama-3.3-70b-versatile"
+    )
+
+# Create or Load ChromaDB
+def create_vector_db():
+    db_path = "./chroma_db"
+
+    if os.path.exists(db_path):
+        embeddings = HuggingFaceBgeEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        return Chroma(persist_directory=db_path, embedding_function=embeddings)
+
+    print("📄 Creating new ChromaDB...")
+    loader = DirectoryLoader("./data", glob="*.pdf", loader_cls=PyPDFLoader)  # Adjusted path
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    texts = text_splitter.split_documents(documents)
+    embeddings = HuggingFaceBgeEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_db = Chroma.from_documents(texts, embeddings, persist_directory=db_path)
+    vector_db.persist()
+    return vector_db
+
+# Setup QA Chain
+def setup_qa_chain(vector_db, llm):
+    retriever = vector_db.as_retriever()
+    prompt_template = """You are a compassionate mental health chatbot. Respond thoughtfully to the following question:
+        {context}
+        User: {question}
+        Chatbot: """
+
+    PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+
+    return RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=retriever,
+        chain_type_kwargs={"prompt": PROMPT}
+    )
+
+# Initialize LLM and QA Chain before using them
+llm = initialize_llm()
+vector_db = create_vector_db()
+qa_chain = setup_qa_chain(vector_db, llm)
+
+# Detect Serious Issues using Transformer Model
+def detect_serious_issue(user_message):
+    result = sentiment_classifier(user_message)[0]
+    negative_sentiment = result["label"] == "NEGATIVE" and result["score"] > 0.7
+    return negative_sentiment
+
+# Fetch Top Psychiatrists Based on Location
+def get_psychiatrists_by_location(state):
+    state = state.strip().lower()
+    return doc_data.get(state, [])
+
+def chatbot_interface(user_message, country, state):
+    try:
+        if user_message.lower() == "exit":
+            return "Chatbot: Take care of yourself. Goodbye! ❤️"
+
+        # Generate chatbot response
+        response = qa_chain.run(user_message)
+
+        # Check for serious issues
+        if detect_serious_issue(user_message):
+            if country.lower() == "india":
+                doctors = get_psychiatrists_by_location(state.lower())
+                if doctors:
+                    doc_info = "\n".join([f"🏥 {doc['name']}\n📍 {doc['hospital']}\n📞 {doc['specialization']}" for doc in doctors])
+                    return f"Chatbot: {response}\n\n🔹 Here are some psychiatrists in {state}:\n{doc_info}"
+                else:
+                    return f"Chatbot: {response}\n\n⚠️ Sorry, no specific doctors found for {state}. Please visit a nearby hospital."
+            else:
+                return f"Chatbot: {response}\n\n⚠️ Currently, psychiatrist details are only available for India."
+
+        return f"Chatbot: {response}"
+
+    except Exception as e:
+        error_message = traceback.format_exc()
+        print("❌ ERROR DETECTED:\n", error_message)
+        return f"⚠️ Error in chatbot: {str(e)}"
+
+gr.Interface(
+    fn=chatbot_interface,
+    inputs=[
+        gr.Textbox(label="Enter your message"),
+        gr.Dropdown(["India", "Other"], label="Country"),
+        gr.Textbox(label="State (if in India)")
+    ],
+    outputs=gr.Textbox(label="Output"),
+    theme="soft"
+).launch()