Update app.py

app.py

@@ -3,70 +3,71 @@ import gradio as gr
 from PyPDF2 import PdfReader
 import requests
 from dotenv import load_dotenv
-
 # Load environment variables
 load_dotenv()
-
 # Get the Hugging Face API token
 HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
-
-def summarize_text(text, instructions):
-    API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
-    headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
-    
-    payload = {
-        "inputs": f"{instructions}\n\nText to summarize:\n{text}",
-        "parameters": {"max_length": 500}
-    }
-    
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()[0]["generated_text"]
-
+def summarize_text(text, instructions, agent_name):
+   print(f"{agent_name}: Starting summarization")
+   API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+   headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
+   payload = {
+       "inputs": f"{instructions}\n\nText to summarize:\n{text}",
+       "parameters": {"max_length": 500}
+   }
+   print(f"{agent_name}: Sending request to API")
+   response = requests.post(API_URL, headers=headers, json=payload)
+   print(f"{agent_name}: Received response from API")
+   return response.json()[0]["generated_text"]
 def process_pdf(pdf_file, chunk_instructions, final_instructions):
-    # Read PDF
-    reader = PdfReader(pdf_file)
-    text = ""
-    for page in reader.pages:
-        text += page.extract_text() + "\n\n"
-    
-    # Chunk the text (simple splitting by pages for this example)
-    chunks = text.split("\n\n")
-    
-    # Agent 1: Summarize each chunk
-    agent1_summaries = []
-    for chunk in chunks:
-        summary = summarize_text(chunk, chunk_instructions)
-        agent1_summaries.append(summary)
-    
-    # Concatenate Agent 1 summaries
-    concatenated_summary = "\n\n".join(agent1_summaries)
-    
-    # Agent 2: Final summarization
-    final_summary = summarize_text(concatenated_summary, final_instructions)
-    
-    return final_summary
-
+   print("Starting PDF processing")
+   # Read PDF
+   reader = PdfReader(pdf_file)
+   text = ""
+   for page in reader.pages:
+       text += page.extract_text() + "\n\n"
+   print(f"Extracted {len(reader.pages)} pages from PDF")
+   # Chunk the text (simple splitting by pages for this example)
+   chunks = text.split("\n\n")
+   print(f"Split text into {len(chunks)} chunks")
+   # Agent 1: Summarize each chunk
+   agent1_summaries = []
+   for i, chunk in enumerate(chunks):
+       print(f"Agent 1: Processing chunk {i+1}/{len(chunks)}")
+       summary = summarize_text(chunk, chunk_instructions, "Agent 1")
+       agent1_summaries.append(summary)
+   print("Agent 1: Finished processing all chunks")
+   # Concatenate Agent 1 summaries
+   concatenated_summary = "\n\n".join(agent1_summaries)
+   print(f"Concatenated Agent 1 summaries (length: {len(concatenated_summary)})")
+   # Agent 2: Final summarization
+   print("Agent 2: Starting final summarization")
+   final_summary = summarize_text(concatenated_summary, final_instructions, "Agent 2")
+   print("Agent 2: Finished final summarization")
+   return final_summary
 def pdf_summarizer(pdf_file, chunk_instructions, final_instructions):
-    if pdf_file is None:
-        return "Please upload a PDF file."
-    
-    try:
-        summary = process_pdf(pdf_file.name, chunk_instructions, final_instructions)
-        return summary
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
+   if pdf_file is None:
+       print("Error: No PDF file uploaded")
+       return "Please upload a PDF file."
+   try:
+       print(f"Starting summarization process for file: {pdf_file.name}")
+       summary = process_pdf(pdf_file.name, chunk_instructions, final_instructions)
+       print("Summarization process completed successfully")
+       return summary
+   except Exception as e:
+       print(f"An error occurred: {str(e)}")
+       return f"An error occurred: {str(e)}"
 # Gradio interface
 iface = gr.Interface(
-    fn=pdf_summarizer,
-    inputs=[
-        gr.File(label="Upload PDF"),
-        gr.Textbox(label="Chunk Instructions", placeholder="Instructions for summarizing each chunk"),
-        gr.Textbox(label="Final Instructions", placeholder="Instructions for final summarization")
-    ],
-    outputs=gr.Textbox(label="Summary"),
-    title="PDF Earnings Summary Generator",
-    description="Upload a PDF of an earnings summary or transcript to generate a concise summary."
+   fn=pdf_summarizer,
+   inputs=[
+       gr.File(label="Upload PDF"),
+       gr.Textbox(label="Chunk Instructions", placeholder="Instructions for summarizing each chunk"),
+       gr.Textbox(label="Final Instructions", placeholder="Instructions for final summarization")
+   ],
+   outputs=gr.Textbox(label="Summary"),
+   title="PDF Earnings Summary Generator",
+   description="Upload a PDF of an earnings summary or transcript to generate a concise summary."
 )
-
+print("Launching Gradio interface")
 iface.launch()