Delete app.py

app.py

@@ -1,96 +0,0 @@
-import os
-import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
-import numpy as np
-from groq import Groq
-
-groq_api_key = os.environ.get("GROQ_API_KEY")
-if groq_api_key is None:
-    raise ValueError("GROQ_API_KEY environment variable not set.")
-
-client = Groq(api_key=groq_api_key)
-
-# Load PubMedBERT for medical queries and embeddings
-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) # Use device=-1 for CPU
-
-# Function to preprocess and understand user query
-def preprocess_query(query):
-    tokens = query.lower().split()
-    keywords = [keyword for keyword in tokens if keyword in ["seizure", "symptoms", "jerks", "confusion", "epilepsy"]] # Example keywords
-    is_medical_related = any(keyword in keywords for keyword in ["seizure", "symptoms", "jerks", "confusion", "epilepsy", "medical"]) # More comprehensive medical keyword check
-    return tokens, keywords, is_medical_related
-
-# Function to generate response (modified for structured output)
-def generate_response(user_query):
-    """
-    Generates a structured response based on the user query, using PubMedBERT for medical analysis when relevant,
-    and Groq for general conversation. Datasets and FAISS are removed in this version.
-    """
-    tokens, keywords, is_medical_related = preprocess_query(user_query)
-
-    # Enhanced Query (for demonstration - can be improved further)
-    enhanced_query = " ".join(tokens) # Basic enhancement - can add synonym expansion etc.
-
-    symptom_insights = "" # FAISS retrieval removed
-
-    if is_medical_related:
-        # Use PubMedBERT for medical-related queries
-        pubmedbert_embeddings = pubmedbert_pipeline(user_query)
-        embedding_mean = np.mean(pubmedbert_embeddings[0], axis=0) # Calculate mean embedding
-
-        pubmedbert_insights = "PubMedBERT analysis: (Embedding vector calculated for medical insight. Note: This version uses feature extraction for analysis and keyword-based recommendations, not direct seizure prediction. For specific predictions or detailed remedies, further fine-tuning and specialized models would be required.)"
-
-        model_name = "PubMedBERT"
-        model_response = pubmedbert_insights
-
-        if "seizure" in keywords or "symptoms" in keywords:
-            remedy_recommendations = "\n\n**General Recommendations & Remedies (Keyword-Based):**\n"
-            remedy_recommendations += "- **Important:** Consult a neurologist for accurate diagnosis and personalized treatment.\n"
-            remedy_recommendations += "- Describe your symptoms in detail to your doctor, including frequency, triggers, and duration.\n"
-            remedy_recommendations += "- Maintain a seizure diary to track events and potential triggers.\n"
-            remedy_recommendations += "- Ensure adequate sleep and stress management as potential seizure triggers.\n"
-            remedy_recommendations += "- Follow your neurologist's advice regarding medication and lifestyle adjustments.\n"
-        else:
-            remedy_recommendations = ""
-    else:
-        # Use LLaMA 2 or Mistral 7B for general conversation (via Groq API)
-        model_name = "LLaMA 2 / Mistral 7B (via Groq)"
-        try:
-            chat_completion = client.chat.completions.create(
-                messages=[{"role": "user", "content": user_query}],
-                model="llama-3.3-70b-versatile", # Or try "mistral-8x7b-32768"
-                stream=False,
-            )
-            model_response = chat_completion.choices[0].message.content.strip()
-        except Exception as e:
-            model_response = f"Error from Groq model: {e}"
-        remedy_recommendations = ""
-
-
-    # Merging insights and generating final structured response
-    final_response = f"**Enhanced Query:** {enhanced_query}\n\n" # Section 1: Enhanced Query
-    final_response += f"**Chatbot Analysis:**\n"
-    final_response += f"- Query Type: {'Medical-related' if is_medical_related else 'General Conversation'}\n"
-    final_response += f"- Model Used: {model_name}\n"
-    final_response += f"- Keywords detected: {', '.join(keywords) if keywords else 'None'}\n" # Keywords section
-
-    final_response += symptom_insights # Symptom insights removed from response
-    final_response += f"\n**Model Response/Insights:**\n{model_response}\n" # Model-specific insights
-    final_response += remedy_recommendations # Section 3: Remedy/Recommendations
-
-    return final_response
-
-# Streamlit Interface
-st.title("Epilepsy & Seizure Chatbot (PubMedBERT & Groq)")
-st.write("Ask questions related to epilepsy and seizures. This chatbot uses PubMedBERT for medical analysis and Groq (LLaMA 2/Mistral) for general conversation. Datasets and FAISS similarity search have been removed.")
-
-user_query = st.text_area("Enter your query here:", placeholder="I'm experiencing sudden muscle jerks and confusion. What could this mean?")
-
-if st.button("Get Response"):
-    if user_query:
-        response = generate_response(user_query)
-        st.markdown(response) # Use st.markdown to render formatted response
-    else:
-        st.warning("Please enter a query.")