Update app.py

app.py

@@ -4,98 +4,129 @@ from PyPDF2 import PdfReader
 import requests
 from dotenv import load_dotenv
 import tiktoken
+
 # Load environment variables
 load_dotenv()
+
 # Get the Hugging Face API token
 HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+
 # Initialize the tokenizer
 tokenizer = tiktoken.get_encoding("cl100k_base")
+
 def count_tokens(text):
-   return len(tokenizer.encode(text))
+    return len(tokenizer.encode(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"]
+    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")
+    
+    # Extracting only the generated summary from the response
+    generated_text = response.json()[0]["generated_text"]
+    
+    # Assuming the model returns the entire input followed by the summary
+    # Split the generated text by the delimiter "\n\n" and take the last part as the summary
+    summary = generated_text.split("\n\n")[-1]
+    
+    return summary
+
 def process_pdf(pdf_file, chunk_instructions, window_instructions, final_instructions):
-   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)})")
-   print(f"Concatenated Summary:{concatenated_summary}")
-   # Sliding window approach
-   window_size = 3500  # in tokens
-   step_size = 3000  # overlap of 500 tokens
-   windows = []
-   current_position = 0
-   while current_position < len(concatenated_summary):
-       window_end = current_position
-       window_text = ""
-       while count_tokens(window_text) < window_size and window_end < len(concatenated_summary):
-           window_text += concatenated_summary[window_end]
-           window_end += 1
-       windows.append(window_text)
-       current_position += step_size
-   print(f"Created {len(windows)} windows for intermediate summarization")
-   # Intermediate summarization
-   intermediate_summaries = []
-   for i, window in enumerate(windows):
-       print(f"Processing window {i+1}/{len(windows)}")
-       summary = summarize_text(window, window_instructions, f"Window {i+1}")
-       intermediate_summaries.append(summary)
-   # Final summarization
-   final_input = "\n\n".join(intermediate_summaries)
-   print(f"Final input length: {count_tokens(final_input)} tokens")
-   final_summary = summarize_text(final_input, final_instructions, "Agent 2")
-   print("Agent 2: Finished final summarization")
-   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)})")
+    print(f"Concatenated Summary: {concatenated_summary}")
+    
+    # Sliding window approach
+    window_size = 3500  # in tokens
+    step_size = 3000  # overlap of 500 tokens
+    windows = []
+    current_position = 0
+    
+    while current_position < len(concatenated_summary):
+        window_end = current_position
+        window_text = ""
+        while count_tokens(window_text) < window_size and window_end < len(concatenated_summary):
+            window_text += concatenated_summary[window_end]
+            window_end += 1
+        windows.append(window_text)
+        current_position += step_size
+    
+    print(f"Created {len(windows)} windows for intermediate summarization")
+    
+    # Intermediate summarization
+    intermediate_summaries = []
+    for i, window in enumerate(windows):
+        print(f"Processing window {i+1}/{len(windows)}")
+        summary = summarize_text(window, window_instructions, f"Window {i+1}")
+        intermediate_summaries.append(summary)
+    
+    # Final summarization
+    final_input = "\n\n".join(intermediate_summaries)
+    print(f"Final input length: {count_tokens(final_input)} tokens")
+    final_summary = summarize_text(final_input, final_instructions, "Agent 2")
+    print("Agent 2: Finished final summarization")
+    
+    return final_summary
+
 def pdf_summarizer(pdf_file, chunk_instructions, window_instructions, final_instructions):
-   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, window_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)}"
+    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, window_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="Window Instructions", placeholder="Instructions for summarizing each window"),
-       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="Window Instructions", placeholder="Instructions for summarizing each window"),
+        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()