Update app.py

app.py

@@ -1,18 +1,33 @@
 import os
-from dotenv import load_dotenv
-import fitz  # PyMuPDF
+import fitz
 from docx import Document
 from sentence_transformers import SentenceTransformer
 import faiss
 import numpy as np
 import pickle
-from langchain_community.llms import HuggingFaceEndpoint  # Might need update
+from langchain_community.llms import HuggingFaceEndpoint
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
 import gradio as gr
+from fastapi import FastAPI
 
-# Load environment variables from .env
-load_dotenv()
+# Initialize FastAPI
+app = FastAPI()
+
+# Function to extract text from a PDF file
+def extract_text_from_pdf(pdf_path):
+    text = ""
+    doc = fitz.open(pdf_path)
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+        text += page.get_text()
+    return text
+
+# Function to extract text from a Word document
+def extract_text_from_docx(docx_path):
+    doc = Document(docx_path)
+    text = "\n".join([para.text for para in doc.paragraphs])
+    return text
 
 # Initialize the embedding model
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -21,15 +36,16 @@ embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
 api_token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
 if not api_token:
     raise ValueError("HUGGINGFACEHUB_API_TOKEN environment variable is not set")
+
 print(f"API Token: {api_token[:5]}...")
 
-# Initialize the HuggingFace LLM (Optional, comment out if not used)
+# Initialize the HuggingFace LLM
 llm = HuggingFaceEndpoint(
     endpoint_url="https://api-inference.huggingface.co/models/gpt2",
     model_kwargs={"api_key": api_token}
 )
 
-# Initialize the HuggingFace embedding
+# Initialize the HuggingFace embeddings
 embedding = HuggingFaceEmbeddings()
 
 # Load or create FAISS index
@@ -43,56 +59,67 @@ else:
     with open(index_path, "wb") as f:
         pickle.dump(index, f)
 
-
-# Function to extract text from a PDF file
-def extract_text_from_pdf(pdf_path):
-    text = ""
-    doc = fitz.open(pdf_path)
-    for page_num in range(len(doc)):
-        page = doc.load_page(page_num)
-        text += page.get_text()
-    return text
-
-
-# Function to extract text from a Word document
-def extract_text_from_docx(docx_path):
-    doc = Document(docx_path)
-    text = "\n".join([para.text for para in doc.paragraphs])
-    return text
-
-def process_and_query(state, text, file=None):
-    # Initialize state on first run
-    if state is None:
-        state = {"processed_text": None, "conversation": []}
-
-    # Check if a file is uploaded
-    if file:
-        # Get the uploaded file content
+def upload_files(files):
+    for file in files:
         content = file.read()
-        if file.filename.endswith('.pdf'):
+        if file.name.endswith('.pdf'):
             with open("temp.pdf", "wb") as f:
                 f.write(content)
-            state["processed_text"] = extract_text_from_pdf("temp.pdf")
-        elif file.filename.endswith('.docx'):
+            text = extract_text_from_pdf("temp.pdf")
+        elif file.name.endswith('.docx'):
             with open("temp.docx", "wb") as f:
                 f.write(content)
-            state["processed_text"] = extract_text_from_docx("temp.docx")
+            text = extract_text_from_docx("temp.docx")
         else:
             return {"error": "Unsupported file format"}
 
-    # Handle user question
-    if state["processed_text"] and text:
-        # Process the question and potentially use LLM for answering (optional)
-        question_embedding = embedding_model.encode([text])
-        # ... (logic to search the index and potentially use LLM for answering)
-        answer = "Answer retrieved from the document based on your question."  # Placeholder answer
+        # Process the text and update FAISS index
+        sentences = text.split("\n")
+        embeddings = embedding_model.encode(sentences)
+        index.add(np.array(embeddings))
+
+    # Save the updated index
+    with open(index_path, "wb") as f:
+        pickle.dump(index, f)
 
-        # Update conversation history
-        state["conversation"].append({"question": text,
-                                         "answer": answer})
+    return "Files processed successfully"
+
+def query_text(text):
+    # Encode the query text
+    query_embedding = embedding_model.encode([text])
+    
+    # Search the FAISS index
+    D, I = index.search(np.array(query_embedding), k=5)
+    
+    top_documents = []
+    for idx in I[0]:
+        if idx != -1:  # Ensure that a valid index is found
+            top_documents.append(f"Document {idx}")
+
+    return top_documents
+
+# 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()
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)
 
-    # Return the conversation history and potentially answer
-    return {"conversation": state["conversation"]}