create app.py

app.py

@@ -0,0 +1,65 @@
+# app.py
+import gradio as gr
+from transformers import pipeline
+
+# --- 1. Load the Text Summarization Model ---
+# We're using a pre-trained summarization model from Hugging Face.
+# 'sshleifer/distilbart-cnn-12-6' is a good balance of speed and quality.
+# The 'pipeline' function simplifies using these models.
+print("Loading summarization model... This may take a moment.")
+try:
+    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    # Fallback or exit if the model can't be loaded, or handle gracefully.
+    # For this demo, we'll let it fail for simplicity in error handling.
+    summarizer = None # Ensure summarizer is None if loading fails
+
+# --- 2. Define the Summarization Function ---
+# This function will be called when the user clicks the 'Summarize' button.
+def summarize_text(input_text):
+    """
+    Takes an input string and returns a concise summary.
+    """
+    if not input_text.strip(): # Check if the input text is empty or just whitespace
+        return "Please enter some text to summarize."
+
+    if summarizer is None:
+        return "Summarization model failed to load. Please try again later or check server logs."
+
+    try:
+        # The summarizer pipeline returns a list of dictionaries.
+        # We're interested in the 'summary_text' key from the first item.
+        summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)
+        return summary[0]['summary_text']
+    except Exception as e:
+        # Basic error handling for summarization issues
+        return f"An error occurred during summarization: {e}"
+
+# --- 3. Create the Gradio Interface ---
+# Gradio makes it easy to build a web UI for machine learning models.
+# `fn`: The function to call when the interface is used.
+# `inputs`: The type of input component (here, a Textbox).
+# `outputs`: The type of output component (here, a Textbox).
+# `title` and `description` are used for the app's heading and explanation.
+iface = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.Textbox(lines=10, placeholder="Paste your text here to get a summary...", label="Input Text"),
+    outputs=gr.Textbox(lines=7, label="Summary"),
+    title="Simple AI Text Summarizer",
+    description=(
+        "This demo application uses a Hugging Face pre-trained model (DistilBART) "
+        "to generate a concise summary of your input text. "
+        "Simply type or paste your text into the box below and click 'Submit'."
+    ),
+    live=False # Set to True if you want real-time summarization as you type
+)
+
+# --- 4. Launch the Gradio App ---
+# This line starts the web server for the Gradio app.
+# share=True generates a public link (useful for sharing demos temporarily).
+# debug=True provides more detailed logging in the console.
+if __name__ == "__main__":
+    iface.launch(share=False, debug=False)
+