Update app.py

app.py

@@ -1,106 +1,112 @@
-# Running on local URL:  http://127.0.0.1:7860
-
-import gradio as gr                 # Gradio: for creating web-based user interfaces
-import PyPDF2                      # PyPDF2: for reading PDF files
-import tempfile                    # tempfile: to safely handle temporary files
-from langchain.prompts import PromptTemplate        # LangChain: for managing prompt templates
-from langchain_huggingface.llms import HuggingFacePipeline  # LangChain integration with HuggingFace models
-
-# Define a summarization class
-class TextSummarizer:
-    def __init__(self):
-        # Define the model to use for summarization
-        self.model_id = "facebook/bart-large-cnn"
-
-    def summarize_text(self, article_text, max_length=150, min_length=30):
-        # Load a summarization pipeline with custom length settings
-        llm = HuggingFacePipeline.from_model_id(
-            model_id=self.model_id,
-            task="summarization",
-            pipeline_kwargs={
-                "max_length": max_length,
-                "min_length": min_length,
-                "do_sample": False  # Deterministic output
-            }
-        )
-
-        # Create a basic prompt template that just passes the text
-        prompt = PromptTemplate(input_variables=["document"], template="""{document}""")
-
-        # Format the article text into the prompt
-        prompt_input = prompt.format(document=article_text)
-
-        # Generate the summary using the model
-        summary = llm.__call__(prompt_input)
-
-        # If the model returns a list of summaries, extract the actual summary text
-        if isinstance(summary, list):
-            return summary[0]['summary_text'] if 'summary_text' in summary[0] else str(summary[0])
-        return str(summary)  # Fallback for other formats
-
-# Function to extract text from an uploaded PDF
-def pdf_to_text(pdf_file):
-    try:
-        # Create a temporary file to write the uploaded PDF bytes
-        with tempfile.NamedTemporaryFile(delete=False) as tmp:
-            tmp.write(pdf_file)      # Write raw bytes directly
-            tmp.flush()              # Make sure data is written to disk
-
-            # Use PyPDF2 to read and extract text
-            reader = PyPDF2.PdfReader(tmp.name)
-            text = "\n".join(page.extract_text() or "" for page in reader.pages)
-
-            # Return cleaned-up text or a message if extraction fails
-            return text.strip() if text.strip() else "No extractable text found in the PDF."
-    except Exception as e:
-        return f"Error reading PDF: {str(e)}"  # Return readable error message
-
-# Instantiate the summarizer class
-summarizer = TextSummarizer()
-
-# Summarize input with user-defined maximum length
-def summarize_input(text, max_words):
-    if not text.strip():
-        return "Please enter or extract some text first."
-
-    try:
-        # Convert max_words input to integer
-        max_length = int(max_words)
-        # Set a safe minimum length for quality summaries
-        min_length = max(30, max_length // 4)
-
-        # Generate the summary
-        return summarizer.summarize_text(text, max_length=max_length, min_length=min_length)
-    except Exception as e:
-        return f"Error during summarization: {str(e)}"
-
-# Build the Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## 📝 Text & PDF Summarizer with Length Control")
-
-    with gr.Row():
-        # Text input for manually entering article
-        text_input = gr.Textbox(label="Enter article text", lines=15, placeholder="Paste your article here...")
-
-        # Upload input for PDF files
-        pdf_file = gr.File(label="Or upload PDF", file_types=[".pdf"], type="binary")
-
-    # User input for controlling max summary length
-    max_words = gr.Number(label="Max summary word count", value=150, precision=0)
-
-    with gr.Row():
-        # Button to convert PDF to text
-        convert_btn = gr.Button("Convert PDF to Text")
-        # Button to generate the summary
-        summary_btn = gr.Button("Summarize Text")
-
-    # Textbox to display the summary output
-    output_text = gr.Textbox(label="Summary", lines=10)
-
-    # Link buttons to their respective functions
-    convert_btn.click(fn=pdf_to_text, inputs=pdf_file, outputs=text_input)
-    summary_btn.click(fn=summarize_input, inputs=[text_input, max_words], outputs=output_text)
-
-# Launch the app if run directly
-if __name__ == "__main__":
-    demo.launch()
+# Running on local URL:  http://127.0.0.1:7860
+
+import gradio as gr                 # Gradio: for creating web-based user interfaces
+import PyPDF2                      # PyPDF2: for reading PDF files
+import tempfile                    # tempfile: to safely handle temporary files
+from langchain.prompts import PromptTemplate        # LangChain: for managing prompt templates
+from langchain_huggingface.llms import HuggingFacePipeline  # LangChain integration with HuggingFace models
+
+# Define a summarization class
+class TextSummarizer:
+    def __init__(self):
+        # Define the model to use for summarization
+        self.model_id = "facebook/bart-large-cnn"
+
+    def summarize_text(self, article_text, max_length=150, min_length=30):
+        # Load a summarization pipeline with custom length settings
+        llm = HuggingFacePipeline.from_model_id(
+            model_id=self.model_id,
+            task="summarization",
+            pipeline_kwargs={
+                "max_length": max_length,
+                "min_length": min_length,
+                "do_sample": False  # Deterministic output
+            }
+        )
+            """pipeline_kwargs = {
+                "max_length": 250,
+                "do_sample": True,
+                "temperature": 0.7,  # More creative
+                "top_k": 50,         # Limit to top 50 tokens
+                "top_p": 0.95        # Use nucleus sampling
+            }"""
+        # Create a basic prompt template that just passes the text
+        prompt = PromptTemplate(input_variables=["document"], template="""{document}""")
+
+        # Format the article text into the prompt
+        prompt_input = prompt.format(document=article_text)
+
+        # Generate the summary using the model
+        summary = llm.__call__(prompt_input)
+
+        # If the model returns a list of summaries, extract the actual summary text
+        if isinstance(summary, list):
+            return summary[0]['summary_text'] if 'summary_text' in summary[0] else str(summary[0])
+        return str(summary)  # Fallback for other formats
+
+# Function to extract text from an uploaded PDF
+def pdf_to_text(pdf_file):
+    try:
+        # Create a temporary file to write the uploaded PDF bytes
+        with tempfile.NamedTemporaryFile(delete=False) as tmp:
+            tmp.write(pdf_file)      # Write raw bytes directly
+            tmp.flush()              # Make sure data is written to disk
+
+            # Use PyPDF2 to read and extract text
+            reader = PyPDF2.PdfReader(tmp.name)
+            text = "\n".join(page.extract_text() or "" for page in reader.pages)
+
+            # Return cleaned-up text or a message if extraction fails
+            return text.strip() if text.strip() else "No extractable text found in the PDF."
+    except Exception as e:
+        return f"Error reading PDF: {str(e)}"  # Return readable error message
+
+# Instantiate the summarizer class
+summarizer = TextSummarizer()
+
+# Summarize input with user-defined maximum length
+def summarize_input(text, max_words):
+    if not text.strip():
+        return "Please enter or extract some text first."
+
+    try:
+        # Convert max_words input to integer
+        max_length = int(max_words)
+        # Set a safe minimum length for quality summaries
+        min_length = max(30, max_length // 4)
+
+        # Generate the summary
+        return summarizer.summarize_text(text, max_length=max_length, min_length=min_length)
+    except Exception as e:
+        return f"Error during summarization: {str(e)}"
+
+# Build the Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 📝 Text & PDF Summarizer with Length Control")
+
+    with gr.Row():
+        # Text input for manually entering article
+        text_input = gr.Textbox(label="Enter article text", lines=15, placeholder="Paste your article here...")
+
+        # Upload input for PDF files
+        pdf_file = gr.File(label="Or upload PDF", file_types=[".pdf"], type="binary")
+
+    # User input for controlling max summary length
+    max_words = gr.Number(label="Max summary word count", value=150, precision=0)
+
+    with gr.Row():
+        # Button to convert PDF to text
+        convert_btn = gr.Button("Convert PDF to Text")
+        # Button to generate the summary
+        summary_btn = gr.Button("Summarize Text")
+
+    # Textbox to display the summary output
+    output_text = gr.Textbox(label="Summary", lines=10)
+
+    # Link buttons to their respective functions
+    convert_btn.click(fn=pdf_to_text, inputs=pdf_file, outputs=text_input)
+    summary_btn.click(fn=summarize_input, inputs=[text_input, max_words], outputs=output_text)
+
+# Launch the app if run directly
+if __name__ == "__main__":
+    demo.launch()