Update app.py

app.py

@@ -1,13 +1,11 @@
 import gradio as gr
 import pdfplumber
-import openai
+import together
 import re
 import unicodedata
-import os
-
-# Set up OpenAI API Key (Replace with your actual key)
-openai.api_key = "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"
 
+# Set up Together.AI API Key (Replace with your actual key)
+together.api_key = "your_together_ai_api_key"
 
 def clean_text(text):
     """Cleans extracted text for better processing by the model."""
@@ -28,42 +26,40 @@ def extract_text_from_pdf(pdf_file):
         return None
 
 def split_text(text, chunk_size=500):
-    """Splits text into smaller chunks for faster processing."""
-    chunks = []
-    for i in range(0, len(text), chunk_size):
-        chunks.append(text[i:i+chunk_size])
-    return chunks
+    """Splits text into smaller chunks for better processing."""
+    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
 
 def chatbot(pdf_file, user_question):
     """Processes the PDF and answers the user's question."""
     
-    # Step 1: Extract text from the PDF
+    # Extract text from the PDF
     text = extract_text_from_pdf(pdf_file)
+    if not text:
+        return "Could not extract any text from the PDF."
     
-    # Step 2: Split into chunks
+    # Split into smaller chunks
     chunks = split_text(text)
-    
-    # Step 3: Use only the first chunk for now (to reduce token usage)
-    if not chunks:
-        return "Could not extract any text from the PDF."
 
+    # Use only the first chunk (to optimize token usage)
     prompt = f"Based on this document, answer the question:\n\nDocument:\n{chunks[0]}\n\nQuestion: {user_question}"
 
-    # Step 4: Send to OpenAI's GPT-3.5
-    response = openai.ChatCompletion.create(
-        model="gpt-4o-mini",
-        messages=[{"role": "user", "content": prompt}]
+    # Send to Together.AI (Mistral-7B)
+    response = together.Completion.create(
+        model="mistralai/Mistral-7B-Instruct-v0.1",
+        prompt=prompt,
+        max_tokens=200,
+        temperature=0.7,
     )
 
-    # Step 5: Return the chatbot's response
-    return response["choices"][0]["message"]["content"]
+    # Return chatbot's response
+    return response["choices"][0]["text"]
 
 # Gradio Interface
 iface = gr.Interface(
     fn=chatbot,
     inputs=[gr.File(label="Upload PDF"), gr.Textbox(label="Ask a Question")],
     outputs=gr.Textbox(label="Answer"),
-    title="PDF Q&A Chatbot"
+    title="PDF Q&A Chatbot (Powered by Together.AI)"
 )
 
 # Launch Gradio app