back to square 1

app.py

@@ -1,33 +1,15 @@
 import gradio as gr
-import requests
-import os
-from dotenv import load_dotenv
+from transformers import pipeline
 from fastapi import FastAPI
 
-# Load environment variables
-load_dotenv()
-HUGGINGFACE_API_URL = os.getenv("HUGGINGFACE_API_URL")
-AUTH_TOKEN = os.getenv("HUGGINGFACE_AUTH_TOKEN")
+# Initialize the summarization pipeline
+summarizer = pipeline("summarization", model="RMWeerasinghe/text_summarization-finetuned_cnn_dailymail")
+
 
 # Define the summarization function
 def summarize_text(input_text):
-    headers = {
-        "Authorization": f"Bearer {AUTH_TOKEN}",
-        "Content-Type": "application/json"
-    }
-    data = {
-        "data": [input_text]  # Ensure input is in the expected format
-    }
-    
-    try:
-        response = requests.post(f"{HUGGINGFACE_API_URL}/api/predict", json=data, headers=headers)
-        response.raise_for_status()  # Raise an error for bad responses
-        result = response.json()
-        return result["data"][0]  # Extract the summary from the response
-    except requests.exceptions.HTTPError as err:
-        return f"Error: {err.response.status_code} - {err.response.text}"
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
+    summary = summarizer(input_text, max_length=600, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
 
 # Create the Gradio app
 app = gr.Interface(