Update app.py

app.py

@@ -1,64 +1,99 @@
 import os
 import streamlit as st
 import pandas as pd
-import subprocess
-
-# Ensure FAISS is installed
-try:
-    import faiss
-except ImportError:
-    subprocess.run(["pip", "install", "faiss-cpu"])
-    import faiss
-
+import faiss
 from sentence_transformers import SentenceTransformer
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 from groq import Groq
 
-# Set up environment variables
+# ✅ Set cache directory
 os.environ["HF_HOME"] = "/tmp/huggingface"
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
 os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/huggingface"
 
-# Load API Key
+# ✅ Securely Fetch API Key
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 if not GROQ_API_KEY:
-    st.error("GROQ_API_KEY is missing. Set it as an environment variable.")
+    st.error("❌ GROQ_API_KEY is missing. Set it as an environment variable.")
     st.stop()
 
 client = Groq(api_key=GROQ_API_KEY)
 
-# Load AI Models
-st.sidebar.header("Loading AI Models... Please Wait ⏳")
+# ✅ Load AI Models
 similarity_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2", cache_folder="/tmp/huggingface")
 embedding_model = SentenceTransformer("all-MiniLM-L6-v2", cache_folder="/tmp/huggingface")
 summarization_model = AutoModelForSeq2SeqLM.from_pretrained("google/long-t5-tglobal-base", cache_dir="/tmp/huggingface")
 summarization_tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base", cache_dir="/tmp/huggingface")
 
-# Load Datasets
+# ✅ Load datasets
 try:
     recommendations_df = pd.read_csv("treatment_recommendations.csv")
     questions_df = pd.read_csv("symptom_questions.csv")
 except FileNotFoundError as e:
-    st.error(f"Missing dataset file: {e}")
+    st.error(f"❌ Missing dataset file: {e}")
     st.stop()
 
-# FAISS Index for Disorders
+# ✅ FAISS Index for Disorder Detection
 treatment_embeddings = similarity_model.encode(recommendations_df["Disorder"].tolist(), convert_to_numpy=True)
 index = faiss.IndexFlatIP(treatment_embeddings.shape[1])
 index.add(treatment_embeddings)
 
-# UI - Streamlit Chatbot
-st.title("MindSpark AI Psychiatrist 💬")
+# ✅ FAISS Index for Question Retrieval
+question_embeddings = embedding_model.encode(questions_df["Questions"].tolist(), convert_to_numpy=True)
+question_index = faiss.IndexFlatL2(question_embeddings.shape[1])
+question_index.add(question_embeddings)
+
+# ✅ Function: Retrieve the most relevant question
+def retrieve_questions(user_input):
+    input_embedding = embedding_model.encode([user_input], convert_to_numpy=True)
+    _, indices = question_index.search(input_embedding, 1)
+    if indices[0][0] == -1:
+        return "I'm sorry, I couldn't find a relevant question."
+    question_block = questions_df["Questions"].iloc[indices[0][0]]
+    return question_block.split(", ")[0] if ", " in question_block else question_block
+
+# ✅ Function: Generate empathetic response using Groq API
+def generate_empathetic_response(user_input, retrieved_question):
+    prompt = f"""
+    The user said: "{user_input}"
+    Relevant Question: - {retrieved_question}
+    You are an empathetic AI psychiatrist. Rephrase this question naturally in a human-like way.
+    """
+    try:
+        response = client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": "You are a helpful, empathetic AI psychiatrist."},
+                {"role": "user", "content": prompt}
+            ],
+            model="llama-3.3-70b-versatile",
+            temperature=0.8,
+            top_p=0.9
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return "I'm sorry, I couldn't process your request."
+
+# ✅ Streamlit UI Setup
+st.title("🧠 MindSpark AI Psychiatric Assistant")
+
+chat_history = st.session_state.get("chat_history", [])
+user_input = st.text_input("Enter your message:")
 
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
+if st.button("Ask AI") and user_input:
+    retrieved_question = retrieve_questions(user_input)
+    empathetic_response = generate_empathetic_response(user_input, retrieved_question)
+    chat_history.append(f"User: {user_input}")
+    chat_history.append(f"AI: {empathetic_response}")
+    st.session_state["chat_history"] = chat_history
 
-user_input = st.text_input("You:", "")
-if st.button("Send"):
-    if user_input:
-        st.session_state.chat_history.append(f"User: {user_input}")
-        st.session_state.chat_history.append(f"AI: [Response]")
+st.subheader("Chat History")
+for msg in chat_history:
+    st.write(msg)
 
-st.write("### Chat History")
-for msg in st.session_state.chat_history[-6:]:
-    st.text(msg)
+if st.button("Summarize Chat"):
+    chat_text = " ".join(chat_history)
+    inputs = summarization_tokenizer("summarize: " + chat_text, return_tensors="pt", max_length=4096, truncation=True)
+    summary_ids = summarization_model.generate(inputs.input_ids, max_length=500, num_beams=4, early_stopping=True)
+    summary = summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    st.subheader("Chat Summary")
+    st.write(summary)