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app.py

@@ -1,15 +1,13 @@
 import streamlit as st
 import fitz  # PyMuPDF
 from transformers import pipeline
-from gtts import gTTS
-import base64
+import pyttsx3
+import tempfile
 import os
-from fpdf import FPDF
-import urllib.parse
 
 # Streamlit page setup
-st.set_page_config(page_title="Health Report Analyzer", page_icon="🩺")
-st.title("🩺 Health Report Analyzer")
+st.set_page_config(page_title="Health Report Analyzer", page_icon="🯪")
+st.title("🯪 Health Report Analyzer")
 
 # Upload PDF
 uploaded_file = st.file_uploader("Upload a Health Report (PDF only)", type="pdf")
@@ -25,34 +23,16 @@ def extract_text(file):
 # Load Hugging Face model for medical explanation
 @st.cache_resource
 def load_explainer():
-    return pipeline("text2text-generation", model="google/flan-t5-base")
-
-# Text-to-Speech helper
-def text_to_speech_base64(text):
-    tts = gTTS(text)
-    tts.save("response.mp3")
-    with open("response.mp3", "rb") as audio_file:
-        audio_bytes = audio_file.read()
-    b64 = base64.b64encode(audio_bytes).decode()
-    audio_html = f"""
-    <audio controls autoplay>
-        <source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
-        Your browser does not support the audio element.
-    </audio>
-    """
-    return audio_html
-
-# PDF Export helper
-def generate_pdf(name, explanation_text):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", "B", 16)
-    pdf.cell(200, 10, "Medical Explanation", ln=True, align="C")
-    pdf.ln(10)
-    pdf.set_font("Arial", size=12)
-    pdf.multi_cell(0, 10, explanation_text)
-    filename = f"{name.replace(' ', '_')}_Explanation.pdf"
-    pdf.output(filename)
+    return pipeline("text2text-generation", model="google/flan-t5-large")
+
+# Text-to-speech function
+def speak_text(text):
+    engine = pyttsx3.init()
+    engine.setProperty('rate', 150)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
+        filename = f.name
+    engine.save_to_file(text, filename)
+    engine.runAndWait()
     return filename
 
 # Main logic
@@ -60,60 +40,42 @@ if uploaded_file:
     text_data = extract_text(uploaded_file)
     st.success("Health Report Uploaded Successfully!")
 
+    # Display the report text
     st.markdown("### 📄 Health Report Content")
     st.write(text_data)
 
     explainer = load_explainer()
+
+    # Store extracted text in session
     st.session_state['report_text'] = text_data
 
+    # Chatbot
     st.subheader("💬 Ask About Any Medical Term or Part of the Report")
 
-    user_question = st.text_input("Enter a medical term or question (e.g. 'CT scan', 'Explain creatinine'):")    
+    user_question = st.text_input("Enter a medical term or question (e.g. 'CT scan', 'Explain creatinine'):")
 
     if st.button("Get AI Explanation") and user_question:
         with st.spinner("Thinking..."):
 
+            # Enhanced prompt for better answers
             prompt = (
-                f"You are a helpful medical assistant. "
-                f"Please explain the following medical term or phrase in very simple words, "
-                f"as if you're talking to someone with no medical background.\n\n"
-                f"Health Report Content:\n{st.session_state['report_text']}\n\n"
-                f"Term or Question: {user_question}\n\n"
-                f"Give a clear, beginner-friendly explanation."
+                f"You are a friendly and experienced medical assistant. "
+                f"Explain this term in very simple language. "
+                f"Provide a short definition and mention why it's important or what it means in a report.\n\n"
+                f"Question: {user_question}"
             )
 
-            response = explainer(prompt)[0]['generated_text']
+            response = explainer(prompt, max_length=300)[0]['generated_text']
 
         st.success("Explanation:")
         st.write(response)
 
-        st.markdown("🔊 **Hear the Explanation**")
-        audio_html = text_to_speech_base64(response)
-        st.markdown(audio_html, unsafe_allow_html=True)
-
-        if os.path.exists("response.mp3"):
-            os.remove("response.mp3")
-
-        st.markdown("---")
-        st.subheader("📤 Export or Share This Explanation")
-
-        user_name = st.text_input("Enter your name to personalize export", value="Patient")
-
-        # PDF Export
-        if st.button("📄 Download as PDF"):
-            pdf_file = generate_pdf(user_name, response)
-            with open(pdf_file, "rb") as f:
-                st.download_button("Download PDF", f, file_name=pdf_file, mime="application/pdf")
-            os.remove(pdf_file)
-
-        # WhatsApp Share
-        whatsapp_msg = urllib.parse.quote(f"Hey! I just got this medical explanation:\n\n{response}")
-        whatsapp_url = f"https://wa.me/?text={whatsapp_msg}"
-        st.markdown(f"[💬 Share via WhatsApp]({whatsapp_url})", unsafe_allow_html=True)
-
-        # Email Copy
-        st.markdown("📧 Copy this text for email:")
-        st.code(f"Subject: Medical Report Explanation\n\nHello,\n\nI wanted to share this:\n\n{response}", language="text")
+        # Play text as speech
+        if st.button("🎧 Listen to Explanation"):
+            audio_file = speak_text(response)
+            audio_bytes = open(audio_file, 'rb').read()
+            st.audio(audio_bytes, format='audio/mp3')
+            os.remove(audio_file)
 
 else:
     st.info("Upload a PDF Health Report to begin.")