Update

app.py

@@ -1,80 +1,82 @@
-from dotenv import load_dotenv
-import os
-import gradio as gr
-import requests
-from newspaper import Article
-import time
-from langdetect import detect
-
-
-# Load environment variables from .env file
-load_dotenv()
-API_KEY = os.getenv('API_KEY')
-
-# Define the Hugging Face API URL
-API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
-
-
-# Set up headers for the API request
-headers = {"Authorization": f"Bearer {API_KEY}"}
-
-# Function to query the Hugging Face API
-def query(payload):
-    for _ in range(5):  # Try 5 times
-        response = requests.post(API_URL, headers=headers, json=payload)
-        result = response.json()
-        if 'error' in result and 'currently loading' in result['error']:
-            time.sleep(5)  # Wait for 5 seconds before retrying
-        else:
-            return result
-    return {"error": "Model is still loading. Please try again later."}
-
-
-
-# Function to summarize text
-def summarize(text, minL=20, maxL=300):
-    output = query({
-        "inputs": text,
-        "parameters": {
-            "min_length": minL,
-            "max_length": maxL
-        }
-    })
-    if "error" in output:
-        return f"Error: {output['error']}"
-    if not isinstance(output, list) or not output:
-        return "Error: Unexpected response format."
-    if "summary_text" not in output[0]:
-        return "Error: 'summary_text' key not found in the response."
-    return output[0]['summary_text']
-
-def Choices(choice,input_text,minL,maxL):
-    if choice=="URL":
-        try:
-            article=Article(input_text,language="en")
-            article.download()
-            article.parse()
-            article.nlp()
-            text=article.text
-        except Exception as e:
-            return f"Error: Unable to fetch article. {str(e)}"
-    else:
-        text=input_text
-    return summarize(text, minL, maxL)
-
-# Create Gradio interface
-demo = gr.Interface(
-    fn=Choices,
-    inputs=[
-        gr.Radio(choices=["URL", "Paragraph"], label="Input Type", value="URL"),
-        gr.Textbox(lines=10, placeholder="Enter text here..."),
-        gr.Number(value=20, label="Minimum Length"),
-        gr.Number(value=300, label="Maximum Length"),
-    ],
-    outputs="textbox",
-    title="Text Summarizer",
-    description="Enter text to summarize using Hugging Face BART model."
-)
-
-# Launch the interface
-demo.launch()
+import os
+import gradio as gr
+import requests
+from newspaper import Article
+import time
+from langdetect import detect
+
+
+# Load environment variables from .env file
+API_KEY = os.getenv('API_KEY')
+
+# Ensure the API key is loaded
+if not API_KEY:
+    raise ValueError("API_KEY is missing. Please set it in the secret variables in the Hugging Face Spaces settings.")
+
+# Define the Hugging Face API URL
+API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
+
+
+# Set up headers for the API request
+headers = {"Authorization": f"Bearer {API_KEY}"}
+
+# Function to query the Hugging Face API
+def query(payload):
+    for _ in range(5):  # Try 5 times
+        response = requests.post(API_URL, headers=headers, json=payload)
+        result = response.json()
+        if 'error' in result and 'currently loading' in result['error']:
+            time.sleep(5)  # Wait for 5 seconds before retrying
+        else:
+            return result
+    return {"error": "Model is still loading. Please try again later."}
+
+
+
+# Function to summarize text
+def summarize(text, minL=20, maxL=300):
+    output = query({
+        "inputs": text,
+        "parameters": {
+            "min_length": minL,
+            "max_length": maxL
+        }
+    })
+    if "error" in output:
+        return f"Error: {output['error']}"
+    if not isinstance(output, list) or not output:
+        return "Error: Unexpected response format."
+    if "summary_text" not in output[0]:
+        return "Error: 'summary_text' key not found in the response."
+    return output[0]['summary_text']
+
+def Choices(choice,input_text,minL,maxL):
+    if choice=="URL":
+        try:
+            article=Article(input_text,language="en")
+            article.download()
+            article.parse()
+            article.nlp()
+            text=article.text
+        except Exception as e:
+            return f"Error: Unable to fetch article. {str(e)}"
+    else:
+        text=input_text
+    return summarize(text, minL, maxL)
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=Choices,
+    inputs=[
+        gr.Radio(choices=["URL", "Paragraph"], label="Input Type", value="URL"),
+        gr.Textbox(lines=10, placeholder="Enter text here..."),
+        gr.Number(value=20, label="Minimum Length"),
+        gr.Number(value=300, label="Maximum Length"),
+    ],
+    outputs="textbox",
+    title="Text Summarizer",
+    description="Enter text to summarize using Hugging Face BART model."
+)
+
+# Launch the interface
+demo.launch()