Update app.py

app.py

@@ -1,46 +1,23 @@
-from fastapi import FastAPI, Request
-from fastapi.middleware.cors import CORSMiddleware
-from transformers import BartTokenizer, BartForConditionalGeneration
 import gradio as gr
+from transformers import BartTokenizer, BartForConditionalGeneration
 import torch
 
-# -------------------------------
-# FASTAPI SETUP
-# -------------------------------
-app = FastAPI(title="Jan Arogya Summarizer API")
-
-# Allow CORS for frontend access
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
 # -------------------------------
 # MODEL LOADING
 # -------------------------------
-model_name = "facebook/bart-large-cnn"
+model_name = "facebook/bart-large-cnn"  # Hugging Face model
 tokenizer = BartTokenizer.from_pretrained(model_name)
 model = BartForConditionalGeneration.from_pretrained(model_name)
 
 # -------------------------------
-# FASTAPI ROUTES (API MODE)
+# SUMMARIZATION FUNCTION
 # -------------------------------
-@app.get("/")
-async def root():
-    return {"message": "🚀 Jan Arogya Summarizer API is live!"}
+def summarize_text(input_text):
+    if not input_text.strip():
+        return "⚠️ Please enter some text to summarize."
 
-@app.post("/summarize")
-async def summarize(request: Request):
-    data = await request.json()
-    text = data.get("text", "")
-    if not text:
-        return {"error": "No text provided."}
+    inputs = tokenizer([input_text], max_length=1024, return_tensors="pt", truncation=True)
 
-    # Summarization process
-    inputs = tokenizer([text], max_length=1024, return_tensors="pt", truncation=True)
     summary_ids = model.generate(
         inputs["input_ids"],
         num_beams=4,
@@ -48,34 +25,28 @@ async def summarize(request: Request):
         max_length=200,
         early_stopping=True
     )
+
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    return {"summary": summary}
+    return summary
 
 # -------------------------------
-# GRADIO INTERFACE (Frontend)
+# GRADIO INTERFACE
 # -------------------------------
-def summarize_text(input_text):
-    if not input_text.strip():
-        return "⚠️ Please enter some text to summarize."
-    inputs = tokenizer([input_text], max_length=1024, return_tensors="pt", truncation=True)
-    summary_ids = model.generate(
-        inputs["input_ids"],
-        num_beams=4,
-        min_length=50,
-        max_length=200,
-        early_stopping=True
-    )
-    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-gradio_interface = gr.Interface(
+demo = gr.Interface(
     fn=summarize_text,
-    inputs=gr.Textbox(lines=10, label="Enter Medical Text"),
-    outputs=gr.Textbox(label="Generated Summary", lines=10),
+    inputs=gr.Textbox(lines=12, placeholder="Paste medical or long text here...", label="Input Text"),
+    outputs=gr.Textbox(lines=10, label="Generated Summary"),
     title="🩺 Jan Arogya Summarizer",
-    description="Summarize long medical text using the facebook/bart-large-cnn model.",
-    theme="soft"
+    description="Summarize long medical or research text using the **facebook/bart-large-cnn** model.",
+    theme="soft",
+    examples=[
+        ["COVID-19 is a respiratory disease caused by the SARS-CoV-2 virus. It spread rapidly across the globe..."],
+        ["Hypertension is a chronic medical condition characterized by persistently high blood pressure levels..."]
+    ],
 )
 
-# Mount Gradio on FastAPI app
-app = gr.mount_gradio_app(app, gradio_interface, path="/")
-
+# -------------------------------
+# LAUNCH
+# -------------------------------
+if __name__ == "__main__":
+    demo.launch()