Update app.py

app.py

@@ -1,90 +1,193 @@
+import gradio as gr
+from transformers import pipeline
+from fpdf import FPDF
 import os
-import faiss
-import streamlit as st
-import numpy as np
-from PIL import Image
 from groq import Groq
-from sentence_transformers import SentenceTransformer
-import easyocr  # ✅ Use EasyOCR instead of Tesseract
-
-# Initialize OCR reader
-reader = easyocr.Reader(["en"])  # Supports multiple languages like ['en', 'ar', 'fr']
-
-# Initialize Sentence Transformer Model
-embed_model = SentenceTransformer("all-MiniLM-L6-v2")
-
-# Initialize FAISS Vector Database
-d = 384  # Embedding dimension
-index = faiss.IndexFlatL2(d)
-
-# Store Extracted Texts
-text_data = []
-
-# Load Groq API Key
-GROQ_API_KEY = os.getenv("mathGpt_groq")
-if GROQ_API_KEY == "your_api_key_here":
-    st.warning("⚠️ Please set your GROQ_API_KEY in Hugging Face secrets.")
-
-groq_client = Groq(api_key=GROQ_API_KEY)
-
-# Function to Process Image and Extract Text using EasyOCR
-def process_image(image):
-    image = np.array(image)  # Convert PIL image to NumPy array
-    results = reader.readtext(image, detail=0)  # Extract text only
-    return " ".join(results).strip()
-
-# Function to Add Text to Vector Database
-def add_text_to_db(text):
-    if text:
-        text_data.append(text)
-        embedding = embed_model.encode([text])
-        index.add(np.array(embedding, dtype=np.float32))
-
-# Function to Retrieve Top-K Relevant Texts
-def retrieve_relevant_texts(query, k=3):
-    if not text_data:
-        return "No notes uploaded yet."
-
-    query_embedding = embed_model.encode([query])
-    distances, indices = index.search(np.array(query_embedding, dtype=np.float32), k)
-
-    relevant_texts = []
-    for i in indices[0]:
-        if i < len(text_data):
-            relevant_texts.append(text_data[i])
-
-    return "\n\n".join(relevant_texts) if relevant_texts else "No relevant information found."
-
-# Function to Generate Response with Groq API
-def generate_response(question):
-    relevant_text = retrieve_relevant_texts(question)
+from deep_translator import GoogleTranslator
+
+# ✅ Load API Keys
+groq_api_key = os.getenv("groq_api_key")  
+groq_client = Groq(api_key=groq_api_key)
+
+# ✅ Load Zero-Shot Disease Classification Model
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+# ✅ Medical Conditions List
+conditions = [
+    "Asthma", "COPD", "Pneumonia", "Tuberculosis", "COVID-19", "Bronchitis",
+    "Heart Failure", "Hypertension", "Diabetes Type 1", "Diabetes Type 2",
+    "Migraine", "Gastroenteritis", "Anemia", "Depression", "Anxiety Disorder",
+    "Chronic Kidney Disease", "UTI", "Osteoporosis", "Psoriasis", "Epilepsy"
+]
+
+# ✅ Specialist Mapping
+specialist_mapping = {
+    "Asthma": ("Pulmonologist", "Respiratory System"),
+    "COPD": ("Pulmonologist", "Respiratory System"),
+    "Pneumonia": ("Pulmonologist", "Respiratory System"),
+    "Tuberculosis": ("Infectious Disease Specialist", "Respiratory System"),
+    "COVID-19": ("Infectious Disease Specialist", "Immune System"),
+    "Heart Failure": ("Cardiologist", "Cardiovascular System"),
+    "Hypertension": ("Cardiologist", "Cardiovascular System"),
+    "Diabetes Type 1": ("Endocrinologist", "Endocrine System"),
+    "Diabetes Type 2": ("Endocrinologist", "Endocrine System"),
+    "Migraine": ("Neurologist", "Nervous System"),
+    "Gastroenteritis": ("Gastroenterologist", "Digestive System"),
+    "Anemia": ("Hematologist", "Blood Disorders"),
+    "Depression": ("Psychiatrist", "Mental Health"),
+    "Anxiety Disorder": ("Psychiatrist", "Mental Health"),
+    "Chronic Kidney Disease": ("Nephrologist", "Urinary System"),
+    "UTI": ("Urologist", "Urinary System"),
+    "Osteoporosis": ("Orthopedic Specialist", "Musculoskeletal System"),
+    "Psoriasis": ("Dermatologist", "Skin Disorders"),
+    "Epilepsy": ("Neurologist", "Nervous System")
+}
+
+# ✅ Translate Function
+def translate_text(text, target_lang="en"):
+    try:
+        return GoogleTranslator(source="auto", target=target_lang).translate(text)
+    except Exception as e:
+        return f"Translation Error: {str(e)}"
+
+# ✅ Expert AI Analysis
+def generate_expert_analysis(condition, symptoms):
+    specialist_title = specialist_mapping.get(condition, ("General Physician", "General Medicine"))[0]
+    prompt = f"""As a {specialist_title.lower()}, explain {condition} to a patient experiencing these symptoms: "{symptoms}".
+    Structure the response into:
+    1. **Biological Process**  
+    2. **Immediate Treatment**  
+    3. **Long-term Care**  
+    4. **Emergency Signs**  
+    5. **Diet Plan**  
+    Use professional yet simple language.
+    """
     response = groq_client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
         model="mixtral-8x7b-32768",
-        messages=[
-            {"role": "system", "content": "You are a math tutor. Answer based on the provided notes."},
-            {"role": "user", "content": f"Math Notes: {relevant_text}\n\nQuestion: {question}"}
-        ],
+        temperature=0.5,
+        max_tokens=1024
     )
     return response.choices[0].message.content
 
-# Streamlit Frontend
-st.title("📖 Math Notes Chatbot 🤖")
-
-# Multi-File Uploader
-uploaded_files = st.file_uploader("Upload Handwritten Math Notes (Images)", type=["png", "jpg", "jpeg"], accept_multiple_files=True)
-
-if uploaded_files:
-    with st.spinner("Processing images..."):
-        for uploaded_file in uploaded_files:
-            image = Image.open(uploaded_file)
-            extracted_text = process_image(image)
-            add_text_to_db(extracted_text)
-    st.success(f"✅ {len(uploaded_files)} images processed and stored!")
+# ✅ Medical Report Generator
+def create_medical_report(symptoms):
+    try:
+        translated_symptoms = translate_text(symptoms, "en")
+        result = classifier(translated_symptoms, conditions, multi_label=False)
+        diagnosis = result['labels'][0]  
+        specialist, system = specialist_mapping.get(diagnosis, ("General Physician", "General Medicine"))
+        expert_analysis = generate_expert_analysis(diagnosis, translated_symptoms)
+
+        full_report = (
+            f"**Medical Report**\n\n"
+            f"**Patient Symptoms:** {translated_symptoms}\n"
+            f"**Primary Diagnosis:** {diagnosis}\n"
+            f"**Affected System:** {system}\n"
+            f"**Consult:** {specialist}\n\n"
+            f"**Expert Analysis:**\n{expert_analysis}\n"
+        )
+
+        pdf = FPDF()
+        pdf.add_page()
+        pdf.set_font("Arial", size=12)
+        pdf.multi_cell(0, 10, full_report)
+        pdf.output("report.pdf")
+
+        return full_report, "report.pdf"
+
+    except Exception as e:
+        return f"Error generating report: {str(e)}", None
+
+# ✅ Medical Chatbot Function
+def medical_chatbot(user_input, chat_history):
+    system_prompt = """You are a compassionate psychiatric assistant named MedMind. Your role is to:
+    - Provide emotional support and stress relief techniques
+    - Offer evidence-based mental health advice
+    - Help users understand medical terminology
+    - Never diagnose but suggest professional help when needed
+    - Maintain therapeutic conversation flow
+    - Remember previous interactions for context"""
+    
+    messages = [
+        {"role": "system", "content": system_prompt},
+        *[{"role": "user" if i%2==0 else "assistant", "content": msg} 
+          for i, msg in enumerate(chat_history)]
+    ]
+    
+    if user_input.strip():
+        messages.append({"role": "user", "content": user_input})
+        
+        response = groq_client.chat.completions.create(
+            messages=messages,
+            model="mixtral-8x7b-32768",
+            temperature=0.7,
+            max_tokens=500
+        )
+        
+        bot_response = response.choices[0].message.content
+        chat_history.append((user_input, bot_response))
+        
+    return "", chat_history
+
+# ✅ Gradio UI with Sidebar
+css = """
+body { background-color: #f0f4f8; }
+.sidebar { background-color: #2c3e50; padding: 20px; height: 100vh; color: white; }
+.chat-container { height: 70vh; overflow-y: auto; }
+.message { padding: 10px; margin: 5px; border-radius: 5px; }
+.user-message { background-color: #e3f2fd; margin-left: 20%; }
+.bot-message { background-color: #ffffff; margin-right: 20%; }
+"""
+
+with gr.Blocks(css=css) as interface:
+    chat_history = gr.State([])
+    
+    with gr.Row():
+        with gr.Column(scale=1, elem_classes="sidebar"):
+            gr.Markdown("## MedExpert AI")
+            with gr.Group():
+                gr.Button("🏠 Home", variant="secondary")
+                gr.Button("🩺 Diagnostic", variant="secondary")
+                gr.Button("💬 Mental Health Chat", variant="secondary")
+        
+        with gr.Column(scale=4):
+            # Home Section
+            with gr.Group(visible=True) as home_section:
+                gr.Markdown("# Welcome to MedExpert AI")
+                gr.Markdown("Your intelligent medical assistant providing:")
+                gr.Markdown("- Symptom Analysis\n- Medical Reports\n- Mental Health Support")
+            
+            # Diagnostic Section
+            with gr.Group(visible=False) as diagnostic_section:
+                symptoms_input = gr.Textbox(label="Describe Your Symptoms")
+                analyze_btn = gr.Button("Analyze Symptoms 🏥", variant="primary")
+                report_output = gr.Textbox(label="Medical Report", interactive=False)
+                pdf_output = gr.File(label="Download PDF Report")
+            
+            # Chatbot Section
+            with gr.Group(visible=False) as chatbot_section:
+                chatbot = gr.Chatbot(elem_classes="chat-container")
+                user_input = gr.Textbox(placeholder="Type your message here...", show_label=False)
+                send_btn = gr.Button("Send", variant="primary")
+    
+    # Diagnostic Logic
+    analyze_btn.click(
+        create_medical_report,
+        inputs=[symptoms_input],
+        outputs=[report_output, pdf_output]
+    )
+    
+    # Chatbot Logic
+    send_btn.click(
+        medical_chatbot,
+        inputs=[user_input, chat_history],
+        outputs=[user_input, chatbot]
+    )
+    user_input.submit(
+        medical_chatbot,
+        inputs=[user_input, chat_history],
+        outputs=[user_input, chatbot]
+    )
 
-# Question Input
-question = st.text_input("🔢 Ask a math question:")
-if question:
-    with st.spinner("Thinking..."):
-        answer = generate_response(question)
-    st.write("### ✨ Answer:")
-    st.write(answer)  # Display plain text answer
+interface.launch()