Update app.py

app.py

@@ -1,71 +1,86 @@
-import gradio as gr 
+import gradio as gr
 import PyPDF2
 import io
-import os
 import time
-from together import Together 
-import textwrap 
+import os
+from together import Together
+import textwrap
 import tempfile
 
-# 1st work is to extract the pdf file 
-def extract_pdf(pdf_file):
-    text= ""
+def extract_text_from_pdf(pdf_file):
+    """Extract text from a PDF file"""
+    text = ""
     try:
-        if hasattr(pdf_file, "read"):  #convert pf into bytes format (data byte bit type)
-            pdf_content= pdf_file.read()
-            if hasattr(pdf_file,"seek"):
+        # Check if the pdf_file is already in bytes format or needs conversion
+        if hasattr(pdf_file, 'read'):
+            # If it's a file-like object (from gradio upload)
+            pdf_content = pdf_file.read()
+            # Reset the file pointer for potential future reads
+            if hasattr(pdf_file, 'seek'):
                 pdf_file.seek(0)
-
         else:
-            #if it already in bites and read
-            pdf_content= pdf_file
-
-        pdf_reader= PyPDF2.PdfReader(io.BytesIO(pdf_content)) #START reading and need to extract the pdf
-
+            # If it's already bytes
+            pdf_content = pdf_file
+            
+        # Read the PDF file
+        pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_content))
+        
+        # Extract text from each page
         for page_num in range(len(pdf_reader.pages)):
-            page_text= pdf_reader.pages[page_num].extract_text()
-            if page_text: # check the extract is working or not
-                print("okay")
-                text= text + page_text + "\n"
+            page_text = pdf_reader.pages[page_num].extract_text()
+            if page_text:  # Check if text extraction worked
+                text += page_text + "\n\n"
             else:
-                print("no extractable text is found there")
-                text += f"[Page{page_num+1}]" 
+                text += f"[Page {page_num+1} - No extractable text found]\n\n"
+        
         if not text.strip():
-            return "no text extracted from PDF it a scanned file or image"
-
+            return "No text could be extracted from the PDF. The document may be scanned or image-based."
+            
         return text
     except Exception as e:
-        return f"Error when extracting text from pdf : {str(e)}"
+        return f"Error extracting text from PDF: {str(e)}"
 
+def format_chat_history(history):
+    """Format the chat history for display"""
+    formatted_history = []
+    for user_msg, bot_msg in history:
+        formatted_history.append((user_msg, bot_msg))
+    return formatted_history
 
-## create a function to chat with the pdf
 def chat_with_pdf(api_key, pdf_text, user_question, history):
+    """Chat with the PDF using Together API"""
     if not api_key.strip():
-        return history + [(user_question,"Error: plase enter your own api key")], history
+        return history + [(user_question, "Error: Please enter your Together API key.")], history
+    
     if not pdf_text.strip() or pdf_text.startswith("Error") or pdf_text.startswith("No text"):
-        return history + [(user_question,"Error: plase upload a valid pdf file with extractable text format")], history
+        return history + [(user_question, "Error: Please upload a valid PDF file with extractable text first.")], history
+    
     if not user_question.strip():
-        return history + [(user_question,"Error: please  enter a valid question")], history
-
+        return history + [(user_question, "Error: Please enter a question.")], history
+    
     try:
-        # model connections all for same
-        client= Together(api_key=api_key) # server e giye hi dibe and server response korbe vhul hole server response korbe na 
-        max_content_len= 150000 # check for the number of word 
-
-        if len(pdf_text) > max_content_len:
-            half_len = max_content_len//2
-            pdf_context= pdf_text[: half_len]+ "\n\n[....Content truncated due to length ....]\n\n" + pdf_text[-half_len:]
+        # Initialize Together client with the API key
+        client = Together(api_key=api_key)
+        
+        # Create the system message with PDF context
+        # Truncate the PDF text if it's too long (model context limit handling)
+        max_context_length = 10000 #10000
+        
+        if len(pdf_text) > max_context_length:
+            # More sophisticated truncation that preserves beginning and end
+            half_length = max_context_length // 2
+            pdf_context = pdf_text[:half_length] + "\n\n[...Content truncated due to length...]\n\n" + pdf_text[-half_length:]
         else:
-            pdf_content= pdf_text
-
-        system_message= f"""You are an intelligent assistant designed to read, understand, and extract information from PDF documents. 
+            pdf_context = pdf_text
+        
+        system_message = f"""You are an intelligent assistant designed to read, understand, and extract information from PDF documents. 
 Based on any question or query the user asks—whether it's about content, summaries, data extraction, definitions, insights, or interpretation—you will 
 analyze the following PDF content and provide an accurate, helpful response grounded in the document. Always respond with clear, concise, and context-aware information.
 PDF CONTENT:
 {pdf_context}
 Answer the user's questions only based on the PDF content above. If the answer cannot be found in the PDF, politely state that the information is not available in the provided document."""
-
-         # Prepare message history for Together API
+        
+        # Prepare message history for Together API
         messages = [
             {"role": "system", "content": system_message},
         ]
@@ -77,47 +92,57 @@ Answer the user's questions only based on the PDF content above. If the answer c
         
         # Add the current user question
         messages.append({"role": "user", "content": user_question})
-
-
-        #connect the model with the app
-        reponse = client.chat.completions.create(
-            model= "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
-            messages= messages,
-            max_tokens= 5000, #5000
-            temperature= .7,
+        
+        # Call the Together API
+        response = client.chat.completions.create(
+            model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+            messages=messages,
+            max_tokens=5000, #5000
+            temperature=0.7,
         )
-        assistance_reponse = response.choices[0].message.content
-
-        # update the chat history
-        new_history= history + [(user_question, assistance_reponse)] #history update purono conversation + current conver= new history
-
+        
+        # Extract the assistant's response
+        assistant_response = response.choices[0].message.content
+        
+        # Update the chat history
+        new_history = history + [(user_question, assistant_response)]
+        
         return new_history, new_history
+    
     except Exception as e:
-        error_message= f"Error:{str(e)}"
+        error_message = f"Error: {str(e)}"
         return history + [(user_question, error_message)], history
 
-# now process the pdf before the chat
 def process_pdf(pdf_file, api_key_input):
+    """Process the uploaded PDF file"""
     if pdf_file is None:
-        return "please upload a PDF file.","",[]
+        return "Please upload a PDF file.", "", []
+    
     try:
-        file_name= os.path.basename(pdf_file.name) if hasattr(pdf_file,"name") else "please Upload"
-        pdf_text= extract_pdf(pdf_file)
-
+        # Get the file name
+        file_name = os.path.basename(pdf_file.name) if hasattr(pdf_file, 'name') else "Uploaded PDF"
+        
+        # Extract text from the PDF
+        pdf_text = extract_text_from_pdf(pdf_file)
+        
+        # Check if there was an error in extraction
         if pdf_text.startswith("Error extracting text from PDF"):
-             return f"❌ {pdf_text}", "", []
+            return f"❌ {pdf_text}", "", []
+        
         if not pdf_text.strip() or pdf_text.startswith("No text could be extracted"):
             return f"⚠️ {pdf_text}", "", []
-
-        word_count= len(pdf_text.split())
+        
+        # Count words for information
+        word_count = len(pdf_text.split())
+        
         # Return a message with the file name and text content
         status_message = f"✅ Successfully processed PDF: {file_name} ({word_count} words extracted)"
-
-        return status_message, pdf_text,[]
+        
+        # Also return an empty history
+        return status_message, pdf_text, []
     except Exception as e:
         return f"❌ Error processing PDF: {str(e)}", "", []
 
-# 
 def validate_api_key(api_key):
     """Simple validation for API key format"""
     if not api_key or not api_key.strip():
@@ -128,66 +153,73 @@ def validate_api_key(api_key):
     
     return "✓ API Key format looks valid (not verified with server)"
 
-
-with gr.Blocks(title="chatsPDFsupport", theme=gr.themes.Ocean()) as app:
+# Create the Gradio interface
+with gr.Blocks(title="ChatPDF with Together AI", theme=gr.themes.Ocean()) as app:
     gr.Markdown("# 📄 ChatPDF with Together AI")
     gr.Markdown("Upload a PDF and chat with it using the Llama-3.3-70B model.")
-
+    
     with gr.Row():
         with gr.Column(scale=1):
+            # API Key input
             api_key_input = gr.Textbox(
                 label="Together API Key",
                 placeholder="Enter your Together API key here...",
-                type="password")
-        # API key validation
+                type="password"
+            )
+            
+            # API key validation
             api_key_status = gr.Textbox(
                 label="API Key Status",
                 interactive=False
             )
-        # PDF upload
+            
+            # PDF upload
             pdf_file = gr.File(
                 label="Upload PDF",
                 file_types=[".pdf"],
                 type="binary"  # Ensure we get binary data
             )
-        # Process PDF button
+            
+            # Process PDF button
             process_button = gr.Button("Process PDF")
             
-        # Status message
-        status_message = gr.Textbox(
-            label="Status",
-            interactive=False
-        )
-        # Hidden field to store the PDF text
-        pdf_text = gr.Textbox(visible=False)
-
-        # Optional: Show PDF preview
-        with gr.Accordion("PDF Content Preview", open=False):
-            pdf_preview = gr.Textbox(
-                label="Extracted Text Preview",
-                interactive=False,
-                max_lines=10,
+            # Status message
+            status_message = gr.Textbox(
+                label="Status",
+                interactive=False
+            )
+            
+            # Hidden field to store the PDF text
+            pdf_text = gr.Textbox(visible=False)
+            
+            # Optional: Show PDF preview
+            with gr.Accordion("PDF Content Preview", open=False):
+                pdf_preview = gr.Textbox(
+                    label="Extracted Text Preview",
+                    interactive=False,
+                    max_lines=10,
+                    show_copy_button=True
+                )
+        
+        with gr.Column(scale=2):
+            # Chat interface
+            chatbot = gr.Chatbot(
+                label="Chat with PDF",
+                height=500,
                 show_copy_button=True
             )
-
-    with gr.Column(scale=2): #right side column row
-        # Chat interface
-        chatbot = gr.Chatbot(
-        label="Chat with PDF",
-        height=500,
-        show_copy_button=True
-        )
-    # Question input
-        question = gr.Textbox(
-            label="Ask a question about the PDF",
-            placeholder="What is the main topic of this document?",
-            lines=2
+            
+            # Question input
+            question = gr.Textbox(
+                label="Ask a question about the PDF",
+                placeholder="What is the main topic of this document?",
+                lines=2
             )
-
-        #submit button 
-        submit_button = gr.Button("Submit Question")
- 
-       # Event handlers
+            
+            # Submit button
+            submit_button = gr.Button("Submit Question")
+    
+    # Event handlers
     def update_preview(text):
         """Update the preview with the first few lines of the PDF text"""
         if not text or text.startswith("Error") or text.startswith("No text"):
@@ -198,7 +230,7 @@ with gr.Blocks(title="chatsPDFsupport", theme=gr.themes.Ocean()) as app:
         if len(text) > 500:
             preview += "...\n[Text truncated for preview. Full text will be used for chat.]"
         return preview
-
+    
     # API key validation event
     api_key_input.change(
         fn=validate_api_key,
@@ -237,3 +269,4 @@ with gr.Blocks(title="chatsPDFsupport", theme=gr.themes.Ocean()) as app:
 # Launch the app
 if __name__ == "__main__":
     app.launch(share=True)
+