Update app.py

app.py

@@ -1,19 +1,17 @@
-def process_and_explain(pdf_file, concept_to_explain):
-    if pdf_file is not None:
-        extracted_text = extract_text_from_pdf(pdf_file)
-        print(f"Extracted Text (first 500 chars):\n{extracted_text[:500]}") # Add this line
-        if "Error reading PDF" in extracted_text or not extracted_text.strip():
-            return "Could not extract text from the PDF. Please try another file."
-       
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
 import PyPDF2
+from sentence_transformers import SentenceTransformer
+import torch
 import logging
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Load the explainer model
+# --- Load Models and Tokenizers ---
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+qa_model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
 explainer = pipeline("text2text-generation", model="google/flan-t5-base")
 
 def extract_text_from_pdf(pdf_file):
@@ -21,52 +19,64 @@ def extract_text_from_pdf(pdf_file):
     try:
         with open(pdf_file.name, 'rb') as pdfFileObj:
             pdfReader = PyPDF2.PdfReader(pdfFileObj)
-            num_pages = len(pdfReader.pages)
-            logger.info(f"Number of pages in PDF: {num_pages}")
-            for pageNum in range(num_pages):
+            for pageNum in range(len(pdfReader.pages)):
                 pageObj = pdfReader.pages[pageNum]
-                page_text = pageObj.extract_text()
-                text += page_text
-            logger.info(f"Extracted {len(text)} characters from PDF.")
+                text += pageObj.extract_text()
     except Exception as e:
         logger.error(f"Error reading PDF: {e}")
-        return f"Error reading PDF: {e}"
+        return None
     return text
 
-def process_and_explain(pdf_file, concept_to_explain):
-    logger.info("Processing request...")
+def chunk_text(text, chunk_size=500, chunk_overlap=50):
+    chunks = []
+    start = 0
+    while start < len(text):
+        end = min(start + chunk_size, len(text))
+        chunks.append(text[start:end])
+        start += chunk_size - chunk_overlap
+    return chunks
+
+def process_and_answer(pdf_file, question):
     if pdf_file is not None:
         extracted_text = extract_text_from_pdf(pdf_file)
-        logger.info(f"Extracted text (first 500 chars):\n{extracted_text[:500]}")
-        if "Error reading PDF" in extracted_text or not extracted_text.strip():
-            return "Could not extract text from the PDF. Please try another file."
+        if not extracted_text:
+            return "Could not extract text from the PDF."
+
+        text_chunks = chunk_text(extracted_text)
+        embeddings = embedding_model.encode(text_chunks)
+
+        question_embedding = embedding_model.encode(question)
+
+        # Simple similarity search (you can use a more efficient method for larger documents)
+        import numpy as np
+        similarities = np.inner(question_embedding, embeddings)
+        most_relevant_chunk_index = np.argmax(similarities)
+        context = text_chunks[most_relevant_chunk_index]
+
+        inputs = tokenizer(question, context, return_tensors="pt", truncation="only", max_length=512)
+        with torch.no_grad():
+            outputs = qa_model(**inputs)
+            answer_start_index = torch.argmax(outputs.start_logits)
+            answer_end_index = torch.argmax(outputs.end_logits) + 1
+            answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start_index:answer_end_index]))
 
-        prompt = f"Explain the concept of '{concept_to_explain}' based on this text: '{extracted_text[:1500]}...' in simple terms."
-        logger.info(f"Prompting explainer model with: {prompt[:200]}...")
-        try:
-            explanation_result = explainer(prompt, max_length=300, do_sample=False)
-            explanation = explanation_result[0]["generated_text"]
-            logger.info(f"Explanation generated: {explanation}")
-            return f"**Extracted Text Snippet:**\n{extracted_text[:500]}...\n\n**Explanation of '{concept_to_explain}':**\n{explanation}"
-        except Exception as e:
-            logger.error(f"Error during explanation generation: {e}")
-            return f"An error occurred while generating the explanation: {e}"
+        return answer.strip() if answer.strip() else "Could not find an answer in the document."
     else:
-        return "Please upload a PDF file."
+        return "Please upload a PDF file and ask a question."
 
 with gr.Blocks() as demo:
-    gr.Markdown("## Explain Concepts from Uploaded PDFs")
-    gr.Markdown("Upload a PDF, and then specify a concept you'd like explained based on its content.")
+    gr.Markdown("## Ask Questions About Your Documents")
+    gr.Markdown("Upload a PDF and ask specific questions about its content.")
 
     pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
-    concept_input = gr.Textbox(label="Concept to Explain", placeholder="E.g., Climate Change")
-    explain_button = gr.Button("Explain Concept")
-    output_text = gr.Markdown(label="Explanation")
+    question_input = gr.Textbox(label="Your Question", placeholder="E.g., Who is the author of this book?")
+    answer_button = gr.Button("Find Answer")
+    output_answer = gr.Textbox(label="Answer")
 
-    explain_button.click(
-        fn=process_and_explain,
-        inputs=[pdf_input, concept_input],
-        outputs=output_text
+    answer_button.click(
+        fn=process_and_answer,
+        inputs=[pdf_input, question_input],
+        outputs=output_answer
     )
 
 demo.launch()