Delete app.py

app.py

@@ -1,93 +0,0 @@
-import os
-import gradio as gr
-import fitz  # PyMuPDF
-from sentence_transformers import SentenceTransformer
-import numpy as np
-import faiss
-from typing import List, Tuple, Dict
-
-# Placeholder for the app's state
-class MyApp:
-    def __init__(self) -> None:
-        self.documents = []
-        self.embeddings = None
-        self.index = None
-        self.model = SentenceTransformer('all-MiniLM-L6-v2')
-
-    def load_pdfs(self, files: List[gr.File]) -> str:
-        """Extracts text from multiple PDF files and stores them."""
-        self.documents = []
-        for file in files:
-            doc = fitz.open(stream=file.read(), filetype="pdf")
-            for page_num in range(len(doc)):
-                page = doc[page_num]
-                text = page.get_text()
-                self.documents.append({
-                    "file_name": file.name,
-                    "page": page_num + 1,
-                    "content": text
-                })
-        return f"Processed {len(files)} PDFs successfully!"
-
-    def build_vector_db(self) -> str:
-        """Builds a vector database using the content of the PDFs."""
-        if not self.documents:
-            return "No documents to process."
-        contents = [doc["content"] for doc in self.documents]
-        self.embeddings = self.model.encode(contents, show_progress_bar=True)
-        self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
-        self.index.add(np.array(self.embeddings))
-        return "Vector database built successfully!"
-
-    def search_documents(self, query: str, k: int = 3) -> List[Dict]:
-        """Searches for relevant document snippets using vector similarity."""
-        if not self.index:
-            return [{"content": "Vector database is not built."}]
-        query_embedding = self.model.encode([query], show_progress_bar=False)
-        D, I = self.index.search(np.array(query_embedding), k)
-        results = [self.documents[i] for i in I[0]]
-        return results if results else [{"content": "No relevant documents found."}]
-
-app = MyApp()
-
-def upload_files(files: List[gr.File]) -> str:
-    return app.load_pdfs(files)
-
-def build_vector_db() -> str:
-    return app.build_vector_db()
-
-def respond(message: str, history: List[Tuple[str, str]]) -> Tuple[List[Tuple[str, str]], str]:
-    # Retrieve relevant documents
-    retrieved_docs = app.search_documents(message)
-    context = "\n".join(
-        [f"File: {doc['file_name']}, Page: {doc['page']}\n{doc['content']}" for doc in retrieved_docs]
-    )
-
-    # Generate response (Placeholder for actual model inference)
-    response_content = f"Simulated response based on the following context:\n{context}"
-
-    # Append the message and generated response to the chat history
-    history.append((message, response_content))
-    return history, ""
-
-with gr.Blocks() as demo:
-    gr.Markdown("# PDF Chatbot")
-    gr.Markdown("Upload your PDFs, build a vector database, and start querying your documents.")
-
-    with gr.Row():
-        with gr.Column():
-            upload_btn = gr.File(label="Upload PDFs", file_types=[".pdf"], file_count="multiple")
-            upload_message = gr.Textbox(label="Upload Status", lines=2)
-            build_db_btn = gr.Button("Build Vector Database")
-            db_message = gr.Textbox(label="DB Build Status", lines=2)
-
-            upload_btn.change(upload_files, inputs=[upload_btn], outputs=[upload_message])
-            build_db_btn.click(build_vector_db, inputs=[], outputs=[db_message])
-
-        with gr.Column():
-            chatbot = gr.Chatbot(label="Chat Responses")
-            query_input = gr.Textbox(label="Enter your query here")
-            submit_btn = gr.Button("Submit")
-            submit_btn.click(respond, inputs=[query_input, chatbot], outputs=[chatbot, query_input])
-
-demo.launch()