Update app.py

app.py

@@ -1,132 +1,64 @@
 import os
 import streamlit as st
 import pandas as pd
-import faiss
+import subprocess
+
+# Ensure FAISS is installed
+try:
+    import faiss
+except ImportError:
+    subprocess.run(["pip", "install", "faiss-cpu"])
+    import faiss
+
 from sentence_transformers import SentenceTransformer
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 from groq import Groq
 
-# ✅ Set up cache directory
+# Set up environment variables
 os.environ["HF_HOME"] = "/tmp/huggingface"
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
 os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/huggingface"
 
-# ✅ Load API Key
+# Load API Key
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 if not GROQ_API_KEY:
-    st.error("❌ 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
+# Load AI Models
 st.sidebar.header("Loading AI Models... Please Wait ⏳")
 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 Disorders
 treatment_embeddings = similarity_model.encode(recommendations_df["Disorder"].tolist(), convert_to_numpy=True)
 index = faiss.IndexFlatIP(treatment_embeddings.shape[1])
 index.add(treatment_embeddings)
 
-# ✅ FAISS Index for Questions
-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)
-
-# ✅ Retrieve 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."
-
-    return questions_df["Questions"].iloc[indices[0][0]]
-
-# ✅ Generate Empathetic Question
-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.
-    Example:
-    - "I understand that anxiety can be overwhelming. Can you tell me more about when you started feeling this way?"
-
-    Generate only one empathetic response.
-    """
-    try:
-        chat_completion = client.chat.completions.create(
-            messages=[{"role": "system", "content": "You are an empathetic AI psychiatrist."},
-                      {"role": "user", "content": prompt}],
-            model="llama-3.3-70b-versatile",
-            temperature=0.8,
-            top_p=0.9
-        )
-        return chat_completion.choices[0].message.content
-    except Exception as e:
-        return "I'm sorry, I couldn't process your request."
-
-# ✅ Disorder Detection
-def detect_disorders(chat_history):
-    full_chat_text = " ".join(chat_history)
-    text_embedding = similarity_model.encode([full_chat_text], convert_to_numpy=True)
-    _, indices = index.search(text_embedding, 3)
-
-    if indices[0][0] == -1:
-        return ["No matching disorder found."]
-
-    return [recommendations_df["Disorder"].iloc[i] for i in indices[0]]
-
-# ✅ Summarization
-def summarize_chat(chat_history):
-    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)
-    return summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-# ✅ UI - Streamlit Chatbot
+# UI - Streamlit Chatbot
 st.title("MindSpark AI Psychiatrist 💬")
 
-# ✅ Chat History
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = []
 
-# ✅ User Input
 user_input = st.text_input("You:", "")
-
 if st.button("Send"):
     if user_input:
-        retrieved_question = retrieve_questions(user_input)
-        empathetic_response = generate_empathetic_response(user_input, retrieved_question)
-
         st.session_state.chat_history.append(f"User: {user_input}")
-        st.session_state.chat_history.append(f"AI: {empathetic_response}")
+        st.session_state.chat_history.append(f"AI: [Response]")
 
-# ✅ Display Chat History
 st.write("### Chat History")
-for msg in st.session_state.chat_history[-6:]:  # Show last 6 messages
+for msg in st.session_state.chat_history[-6:]:
     st.text(msg)
-
-# ✅ Summarization & Disorder Detection
-if st.button("Summarize Chat"):
-    summary = summarize_chat(st.session_state.chat_history)
-    st.write("### Chat Summary")
-    st.text(summary)
-
-if st.button("Detect Disorders"):
-    disorders = detect_disorders(st.session_state.chat_history)
-    st.write("### Possible Disorders")
-    st.text(", ".join(disorders))