Create app.py

app.py

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+import os
+import streamlit as st
+import pandas as pd
+import faiss
+from sentence_transformers import SentenceTransformer
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from groq import Groq
+
+# ✅ Set up cache directory
+os.environ["HF_HOME"] = "/tmp/huggingface"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
+os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/huggingface"
+
+# ✅ 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.stop()
+
+client = Groq(api_key=GROQ_API_KEY)
+
+# ✅ 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
+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.stop()
+
+# ✅ 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
+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}")
+
+# ✅ Display Chat History
+st.write("### Chat History")
+for msg in st.session_state.chat_history[-6:]:  # Show last 6 messages
+    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))