Update app.py

app.py

@@ -1,93 +1,173 @@
 import os
-import json
-import time
-import speech_recognition as sr
-import pyttsx3
-from transformers import pipeline, AutoTokenizer, AutoModel
-
-# Initialize Text-to-Speech (TTS)
-tts_engine = pyttsx3.init()
-tts_engine.setProperty("rate", 150)  # Adjust speaking speed
-
-def speak(text):
-    """Convert text to speech."""
-    tts_engine.say(text)
-    tts_engine.runAndWait()
-
-# Initialize Speech Recognition (STT)
-recognizer = sr.Recognizer()
-
-def listen():
-    """Capture user's voice input and convert to text."""
-    with sr.Microphone() as source:
-        print("🎤 Listening...")
-        recognizer.adjust_for_ambient_noise(source)
-        audio = recognizer.listen(source)
-    
-    try:
-        user_input = recognizer.recognize_google(audio)
-        print(f"User: {user_input}")
-        return user_input
-    except sr.UnknownValueError:
-        speak("Sorry, I didn't catch that. Can you repeat?")
-        return listen()
-    except sr.RequestError:
-        speak("There was an error with the speech recognition service.")
-        return ""
-
-# Load Medical Model
+import streamlit as st
+import numpy as np
+import faiss
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModel
+from groq import Groq
+import sounddevice as sd
+import soundfile as sf
+import tempfile
+import whisper
+
+# Load API Key from Environment
+groq_api_key = os.environ.get("GROQ_API_KEY")
+if groq_api_key is None:
+    st.error("GROQ_API_KEY environment variable not set.")
+    st.stop()
+
+# Initialize Groq Client
+try:
+    client = Groq(api_key=groq_api_key)
+except Exception as e:
+    st.error(f"Error initializing Groq client: {e}")
+    st.stop()
+
+# Load PubMedBERT Model (Try Groq API first, then Hugging Face)
+try:
+    pubmedbert_tokenizer = AutoTokenizer.from_pretrained("NeuML/pubmedbert-base-embeddings")
+    pubmedbert_model = AutoModel.from_pretrained("NeuML/pubmedbert-base-embeddings")
+    pubmedbert_pipeline = pipeline('feature-extraction', model=pubmedbert_model, tokenizer=pubmedbert_tokenizer, device=-1)
+except Exception:
+    st.warning("Error loading PubMedBERT from Groq API. Using Hugging Face model.")
+    pubmedbert_tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext")
+    pubmedbert_model = AutoModelForSequenceClassification.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext")
+    pubmedbert_pipeline = pipeline('feature-extraction', model=pubmedbert_model, tokenizer=pubmedbert_tokenizer, device=-1)
+
+# Initialize FAISS Index
+embedding_dim = 768
+index = faiss.IndexFlatL2(embedding_dim)
+
+# Load Whisper model
 try:
-    tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext")
-    model = AutoModel.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext")
-    medical_pipeline = pipeline('feature-extraction', model=model, tokenizer=tokenizer, device=-1)
+    whisper_model = whisper.load_model("base") # or "small", "medium", "large"
 except Exception as e:
-    speak("Error loading medical AI model. Using fallback response.")
-    medical_pipeline = None
-
-# Store conversation in JSON
-conversation_data = {"user_info": {}, "medical_history": [], "chat": []}
-
-def save_conversation():
-    with open("conversation.json", "w") as f:
-        json.dump(conversation_data, f, indent=4)
-
-# Collect Basic User Info
-speak("Hello! I am your medical assistant. May I know your name?")
-user_name = listen()
-conversation_data["user_info"]["name"] = user_name
-
-speak(f"Nice to meet you, {user_name}. How old are you?")
-age = listen()
-conversation_data["user_info"]["age"] = age
-
-speak("What is your primary health concern today?")
-health_issue = listen()
-conversation_data["medical_history"].append(health_issue)
-
-# Real-Time Conversation Loop
-while True:
-    speak("Tell me more about your symptoms or concerns.")
-    user_query = listen()
-    
-    if "stop" in user_query.lower() or "bye" in user_query.lower():
-        speak("Thank you for sharing your details. Take care!")
-        break
-    
-    conversation_data["chat"].append({"user": user_query})
-    
-    # Process Medical Query
-    if medical_pipeline:
+    st.error(f"Error loading Whisper model: {e}")
+    st.stop()
+
+# Function to Check if Query is Related to Epilepsy
+def preprocess_query(query):
+    tokens = query.lower().split()
+    epilepsy_keywords = ["seizure", "epilepsy", "convulsion", "neurology", "brain activity"]
+    is_epilepsy_related = any(k in tokens for k in epilepsy_keywords)
+    return tokens, is_epilepsy_related
+
+# Function to Generate Response
+def generate_response(user_query): # Removed chat_history argument
+    # Grammatical Correction using LLaMA (Hidden from User)
+    try:
+        correction_prompt = f"""
+        Correct the following user query for grammar and spelling errors, but keep the original intent intact.
+        Do not add or remove any information, just fix the grammar.
+        User Query: {user_query}
+        Corrected Query:
+        """
+        grammar_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": correction_prompt}],
+            model="llama-3.3-70b-versatile",
+            stream=False,
+        )
+        corrected_query = grammar_completion.choices[0].message.content.strip()
+        if not corrected_query:
+            corrected_query = user_query
+    except Exception as e:
+        corrected_query = user_query
+        print(f"⚠️ Grammar correction error: {e}")
+
+    tokens, is_epilepsy_related = preprocess_query(corrected_query)
+
+    # Greeting Responses
+    greetings = ["hello", "hi", "hey"]
+    if any(word in tokens for word in greetings):
+        return "👋 Hello! How can I assist you today?"
+
+    # Epilepsy Related Response
+    if is_epilepsy_related:
+        try:
+            pubmedbert_embeddings = pubmedbert_pipeline(corrected_query)
+            embedding_mean = np.mean(pubmedbert_embeddings[0], axis=0)
+            index.add(np.array([embedding_mean]))
+            pubmedbert_insights = "**PubMedBERT Analysis:** ✅ Query is relevant to epilepsy research."
+        except Exception as e:
+            pubmedbert_insights = f"⚠️ Error during PubMedBERT analysis: {e}"
+
         try:
-            embeddings = medical_pipeline(user_query)
-            response = "Based on medical analysis, I recommend consulting a doctor for further evaluation."
-        except Exception:
-            response = "I couldn't analyze your query, but I suggest seeking medical advice."
+            epilepsy_prompt = f"""
+            **User Query:** {corrected_query}
+            **Instructions:** Provide a concise, structured, and human-friendly response specifically about epilepsy or seizures.
+            """
+            chat_completion = client.chat.completions.create(
+                messages=[{"role": "user", "content": epilepsy_prompt}],
+                model="llama-3.3-70b-versatile",
+                stream=False,
+            )
+            model_response = chat_completion.choices[0].message.content.strip()
+        except Exception as e:
+            model_response = f"⚠️ Error generating response with LLaMA: {e}"
+
+        return f"**NeuroGuard:** ✅ **Analysis:**\n{pubmedbert_insights}\n\n**Response:**\n{model_response}"
+
+    # General Health Response
     else:
-        response = "I can assist with basic health guidance. Please consult a medical professional for proper diagnosis."
-    
-    conversation_data["chat"].append({"bot": response})
-    speak(response)
-    save_conversation()
-
-# Save final conversation
-save_conversation()
+        try:
+            pubmedbert_embeddings = pubmedbert_pipeline(corrected_query)
+            embedding_mean = np.mean(pubmedbert_embeddings[0], axis=0)
+            index.add(np.array([embedding_mean]))
+            pubmedbert_insights = "**PubMedBERT Analysis:** PubMed analysis performed for health-related context."
+        except Exception as e:
+            pubmedbert_insights = f"⚠️ Error during PubMedBERT analysis: {e}"
+
+        try:
+            general_health_prompt = f"""
+            **User Query:** {corrected_query}
+            **Instructions:** Provide a concise, structured, and human-friendly response to the general health query. If the query is clearly not health-related, respond generally.
+            """
+            chat_completion = client.chat.completions.create(
+                messages=[{"role": "user", "content": general_health_prompt}],
+                model="llama-3.3-70b-versatile",
+                stream=False,
+            )
+            model_response = chat_completion.choices[0].message.content.strip()
+        except Exception as e:
+            model_response = f"⚠️ Error generating response with LLaMA: {e}"
+
+        return f"**NeuroGuard:** ✅ **Analysis:**\n{pubmedbert_insights}\n\n**Response:**\n{model_response}"
+
+# Streamlit UI Setup
+st.set_page_config(page_title="NeuroGuard: Voice Call Health Assistant", layout="wide")
+st.title("📞 NeuroGuard: Voice Call Health Assistant")
+st.write("🎙️ Click 'Start Recording', speak your question, and NeuroGuard will respond.")
+
+bot_response_area = st.empty() # For bot response display
+
+if st.button("Start Recording"):
+    st.write("Recording... Speak now.")
+    fs = 44100
+    seconds = 10
+    try:
+        myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=1)
+        sd.wait()
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
+            sf.write(temp_audio.name, myrecording, fs)
+            audio_file = open(temp_audio.name, 'rb')
+            audio_bytes = audio_file.read()
+
+        st.audio(audio_bytes, format="audio/wav")
+
+        with st.spinner("Transcribing audio..."):
+            temp_audio_path = temp_audio.name
+            transcription = whisper_model.transcribe(temp_audio_path)
+            user_query = transcription["text"]
+
+        st.write(f"**You said:** {user_query}")
+
+        with bot_response_area.container():
+            with st.chat_message("bot"):
+                with st.spinner("🤖 Thinking..."):
+                    try:
+                        response = generate_response(user_query) # Removed chat_history argument
+                        st.markdown(response)
+                    except Exception as e:
+                        st.error(f"⚠️ Error processing query: {e}")
+
+    except Exception as e:
+        st.error(f"⚠️ Recording error: {e}")