Update app.py

app.py

@@ -1,26 +1,26 @@
+
+
 import os
-import fitz
 from docx import Document
 from sentence_transformers import SentenceTransformer
 import faiss
 import numpy as np
 import pickle
-import gradio as gr
 from langchain_community.llms import HuggingFaceEndpoint
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
 
-# Function to extract text from a PDF file
-def extract_text_from_pdf(pdf_path):
-    text = ""
-    try:
-        doc = fitz.open(pdf_path)
-        for page_num in range(len(doc)):
-            page = doc.load_page(page_num)
-            text += page.get_text()
-    except Exception as e:
-        print(f"Error extracting text from PDF: {e}")
-    return text
+# Initialize the embedding model
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Initialize the HuggingFace LLM
+llm = HuggingFaceEndpoint(
+    endpoint_url="https://api-inference.huggingface.co/models/gpt2",
+    model_kwargs={"api_key": os.getenv('HUGGINGFACEHUB_API_TOKEN')}
+)
+
+# Initialize the HuggingFace embeddings
+embedding = HuggingFaceEmbeddings()
 
 # Function to extract text from a Word document
 def extract_text_from_docx(docx_path):
@@ -32,23 +32,6 @@ def extract_text_from_docx(docx_path):
         print(f"Error extracting text from DOCX: {e}")
     return text
 
-# Initialize the embedding model
-embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# Hugging Face API token
-api_token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
-if not api_token:
-    raise ValueError("HUGGINGFACEHUB_API_TOKEN environment variable is not set or invalid")
-
-# Initialize the HuggingFace LLM
-llm = HuggingFaceEndpoint(
-    endpoint_url="https://api-inference.huggingface.co/models/gpt-3.5-turbo",
-    model_kwargs={"api_key": api_token}
-)
-
-# Initialize the HuggingFace embeddings
-embedding = HuggingFaceEmbeddings()
-
 # Load or create FAISS index
 index_path = "faiss_index.pkl"
 document_texts_path = "document_texts.pkl"
@@ -81,22 +64,15 @@ def upload_files(files):
     try:
         for file in files:
             file_path = file.name  # Get the file path from the NamedString object
-            if file_path.endswith('.pdf'):
-                text = extract_text_from_pdf(file_path)
-            elif file_path.endswith('.docx'):
+            if file_path.endswith('.docx'):
                 text = extract_text_from_docx(file_path)
-            else:
-                return "Unsupported file format"
-
-            print(f"Extracted text: {text[:100]}...")  # Debug: Show the first 100 characters of the extracted text
 
-            # Process the text and update FAISS index
-            sentences = text.split("\n")
-            sentences = [preprocess_text(sentence) for sentence in sentences if sentence.strip()]
-            embeddings = embedding_model.encode(sentences)
-            print(f"Embeddings shape: {embeddings.shape}")  # Debug: Show the shape of the embeddings
-            index.add(np.array(embeddings))
-            document_texts.extend(sentences)  # Store sentences for retrieval
+                # Process the text and update FAISS index
+                sentences = text.split("\n")
+                sentences = [preprocess_text(sentence) for sentence in sentences if sentence.strip()]
+                embeddings = embedding_model.encode(sentences)
+                index.add(np.array(embeddings))
+                document_texts.extend(sentences)  # Store sentences for retrieval
 
         # Save the updated index and documents
         with open(index_path, "wb") as f:
@@ -111,49 +87,23 @@ def upload_files(files):
         print(f"Error processing files: {e}")
         return f"Error processing files: {e}"
 
-# Improved prompt template
-prompt_template = """
-You are a helpful assistant. Use the provided context to answer the question accurately. 
-If the answer is not in the context, say "answer is not available in the context". 
-Do not provide false information.
-
-Context:
-{context}
-
-Question:
-{question}
-
-Answer:
-"""
-
 def query_text(text):
     try:
-        print(f"Query text: {text}")  # Debug: Show the query text
-
         # Encode the query text
         query_embedding = embedding_model.encode([text])
-        print(f"Query embedding shape: {query_embedding.shape}")  # Debug: Show the shape of the query embedding
         
         # Search the FAISS index
         D, I = index.search(np.array(query_embedding), k=5)
-        print(f"Distances: {D}, Indices: {I}")  # Debug: Show the distances and indices of the search results
         
         top_documents = []
         for idx in I[0]:
             if idx != -1 and idx < len(document_texts):  # Ensure that a valid index is found
                 top_documents.append(document_texts[idx])  # Append the actual sentences for the response
-            else:
-                print(f"Invalid index found: {idx}")
-        
-        # Remove duplicates and sort by relevance
-        top_documents = list(dict.fromkeys(top_documents))
         
-        # Join the top documents for the context
+        # Prepare the prompt
         context = "\n".join(top_documents)
+        prompt = f"Context:\n{context}\n\nQuestion:\n{text}\n\nAnswer:\n"
 
-        # Prepare the prompt
-        prompt = prompt_template.format(context=context, question=text)
-        
         # Query the LLM
         response = llm(prompt)
         return response
@@ -161,23 +111,21 @@ def query_text(text):
         print(f"Error querying text: {e}")
         return f"Error querying text: {e}"
 
-# Create Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("## Document Upload and Query System")
-    
-    with gr.Tab("Upload Files"):
-        upload = gr.File(file_count="multiple", label="Upload PDF or DOCX files")
-        upload_button = gr.Button("Upload")
-        upload_output = gr.Textbox()
-        upload_button.click(fn=upload_files, inputs=upload, outputs=upload_output)
-    
-    with gr.Tab("Query"):
-        query = gr.Textbox(label="Enter your query")
-        query_button = gr.Button("Search")
-        query_output = gr.Textbox()
-        query_button.click(fn=query_text, inputs=query, outputs=query_output)
-
-demo.launch()
+# Sample Gradio integration (for illustration)
+import gradio as gr
+
+def main():
+    gr.Interface(
+        [upload_files, query_text],
+        ["files", "text"],
+        ["text", "text"],
+        title="Document Upload and Query System",
+        description="Upload DOCX files to build an index, then query for answers based on uploaded documents.",
+    ).launch()
+
+if __name__ == "__main__":
+    main()
+