Create app.py

app.py

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+import fitz  # PyMuPDF
+from sentence_transformers import SentenceTransformer
+import numpy as np
+import faiss
+from typing import List, Dict
+
+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, file_paths: List[str]) -> None:
+        """Extracts text from multiple PDF files and stores them."""
+        self.documents = []
+        for file_path in file_paths:
+            doc = fitz.open(file_path)
+            for page_num in range(len(doc)):
+                page = doc[page_num]
+                text = page.get_text()
+                self.documents.append({"file": file_path, "page": page_num + 1, "content": text})
+        print("PDFs processed successfully!")
+
+    def build_vector_db(self) -> None:
+        """Builds a vector database using the content of the PDFs."""
+        if not self.documents:
+            print("No documents to process.")
+            return
+        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))
+        print("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:
+            print("Vector database is not built.")
+            return []
+        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."}]
+
+  import gradio as gr
+from typing import List, Tuple
+
+app = MyApp()
+
+def upload_files(files) -> str:
+    file_paths = [file.name for file in files]
+    app.load_pdfs(file_paths)
+    return f"Uploaded {len(files)} files successfully."
+
+def build_vector_db() -> str:
+    app.build_vector_db()
+    return "Vector database built successfully!"
+
+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']}, Page: {doc['page']}\n{doc['content']}" for doc in retrieved_docs])
+
+    # Generate response using the generative model
+    # Assuming you have set up the generative model as in your original code
+    # Replace the following line with your model's response generation
+    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()