Update app.py

app.py

@@ -1,2 +1,111 @@
 import streamlit as st
-st.title("hello")
+import google.generativeai as genai
+import chromadb
+import os
+import time
+from transformers import pipeline
+from dotenv import load_dotenv
+
+# Load API key from .env
+load_dotenv()
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+
+# Configure Gemini AI
+genai.configure(api_key=GEMINI_API_KEY)
+model = genai.GenerativeModel("gemini-1.5-pro")
+
+# Initialize ChromaDB for RAG
+client = chromadb.PersistentClient(path="./mental_health_memory")
+collection = client.get_or_create_collection(name="chat_history")
+
+# Load Sentiment Analysis Model
+sentiment_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", return_all_scores=True)
+
+# Function to analyze sentiment
+def analyze_sentiment(text):
+    results = sentiment_pipeline(text)
+    emotions = {res["label"]: res["score"] for res in results[0]}
+    return max(emotions, key=emotions.get), emotions
+
+# Function to store chat history in ChromaDB
+def store_chat(user_input, bot_response):
+    collection.add(
+        documents=[user_input, bot_response],
+        metadatas=[{"role": "user"}, {"role": "bot"}],
+        ids=[str(len(collection.get())) + "_user", str(len(collection.get())) + "_bot"]
+    )
+
+# Function to retrieve relevant past messages (RAG)
+def retrieve_context():
+    history = collection.get()
+    if len(history["documents"]) > 3:
+        return history["documents"][-3:]
+    return history["documents"]
+
+# Function to generate a response using LLM with past context
+def get_gemini_response(user_input):
+    past_context = retrieve_context()
+    full_prompt = f"Previous Chat Context: {past_context}\nUser: {user_input}\nBot:"
+    try:
+        response = model.generate_content(full_prompt)
+        return response.text
+    except Exception as e:
+        return f"Sorry, I encountered an issue. Error: {str(e)}"
+
+# Streamlit UI
+st.title("\U0001F9E0 Mental Health Chatbot")
+
+# Chat history session
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Display chat history
+for role, message in st.session_state.chat_history:
+    with st.chat_message(role):
+        st.write(message)
+
+# User input
+user_input = st.chat_input("Ask me anything about mental health...")
+
+if user_input:
+    st.chat_message("user").write(user_input)
+    
+    # Analyze sentiment
+    emotion, scores = analyze_sentiment(user_input)
+    
+    # Generate chatbot response with RAG
+    with st.spinner("Thinking..."):
+        bot_response = get_gemini_response(user_input)
+    
+    st.chat_message("assistant").write(bot_response)
+    
+    # Store chat history
+    st.session_state.chat_history.append(("user", user_input))
+    st.session_state.chat_history.append(("assistant", bot_response))
+    store_chat(user_input, bot_response)
+
+# Sidebar: Clear Chat & Instructions
+with st.sidebar:
+    if st.button("Clear Chat"):
+        st.session_state.chat_history = []
+        collection.delete(ids=collection.get()["ids"])
+        st.success("Chat cleared! Refreshing...")
+        st.rerun()
+
+
+
+        
+    
+    st.markdown("---")
+    st.markdown("*Note: This chatbot is for informational purposes only and should not replace professional mental health advice.*")
+
+# Sentiment Analysis Button
+if st.button("Sentiment Analysis"):
+    chat_text = " ".join([msg for _, msg in st.session_state.chat_history])
+    if chat_text:
+        detected_emotion, emotion_scores = analyze_sentiment(chat_text)
+        st.subheader("Sentiment Analysis Result")
+        st.write(f"Detected Emotion: **{detected_emotion}**")
+        st.json(emotion_scores)
+    else:
+        st.warning("No chat history found for analysis.")