Update app.py

app.py

@@ -3,7 +3,7 @@ import fitz  # PyMuPDF
 from gtts import gTTS
 import tempfile
 import os
-from transformers import pipeline
+import requests
 
 # Streamlit page setup
 st.set_page_config(page_title="Health Report Analyzer", page_icon="🩺")
@@ -20,10 +20,19 @@ def extract_text(file):
         text += page.get_text()
     return text
 
-# Load BioGPT model for detailed medical explanation
-@st.cache_resource
-def load_medical_explainer():
-    return pipeline("text2text-generation", model="microsoft/BioGPT-Large")
+# Ask medical question using Hugging Face Inference API (BioGPT)
+API_URL = "https://api-inference.huggingface.co/models/microsoft/BioGPT-Large"
+headers = {"Content-Type": "application/json"}
+
+def ask_medical_question(question):
+    payload = {
+        "inputs": f"Explain this medical concept in detail for a general audience: {question}"
+    }
+    response = requests.post(API_URL, headers=headers, json=payload)
+    if response.status_code == 200:
+        return response.json()[0]['generated_text']
+    else:
+        return "⚠️ Sorry, something went wrong while contacting the medical AI."
 
 # Doctor recommendation logic
 def suggest_doctor(term):
@@ -38,7 +47,7 @@ def suggest_doctor(term):
         return "A **diabetologist** or **endocrinologist** is recommended for sugar level issues."
     return "Please consult a **general physician** for further evaluation."
 
-# Normal ranges (very basic examples)
+# Basic reference ranges
 def get_reference_range(term):
     term = term.lower()
     if "hemoglobin" in term:
@@ -52,24 +61,21 @@ def get_reference_range(term):
 # Main logic
 if uploaded_file:
     text_data = extract_text(uploaded_file)
-    st.success("Health Report Uploaded Successfully!")
+    st.success("✅ Health Report Uploaded Successfully!")
 
     # Display the report text
     st.markdown("### 📄 Health Report Content")
     st.write(text_data)
 
-    explainer = load_medical_explainer()
     st.session_state['report_text'] = text_data
 
     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'):", key="question")
 
     if st.button("Get AI Explanation") and user_question:
         with st.spinner("Thinking..."):
-            prompt = f"Explain this medical concept in detail for a general audience: {user_question}"
-            response = explainer(prompt, max_length=512)[0]['generated_text']
-            explanation = response
+            explanation = ask_medical_question(user_question)
             range_info = get_reference_range(user_question)
             suggestion = suggest_doctor(user_question)
 
@@ -88,4 +94,4 @@ if uploaded_file:
             st.audio(audio_file.read(), format='audio/mp3')
 
 else:
-    st.info("Upload a PDF Health Report to begin.")
+    st.info("📂 Upload a PDF Health Report to begin.")