create .py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+import fitz  # PyMuPDF for PDF handling
+import faiss
+import numpy as np
+
+# Load models for embeddings and QA
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
+
+# Global variables to store documents and index
+documents, passages, embeddings, file_names, indexes, index = {}, [], None, [], [], None
+
+# Function to extract text from uploaded PDFs
+def upload_and_extract_text(files):
+    global documents
+    documents = {}
+    
+    for file in files:
+        with fitz.open(file.name) as pdf:
+            text = ""
+            for page in pdf:
+                text += page.get_text("text")
+            documents[file.name] = text
+    return "PDF content extracted and indexed successfully."
+
+# Function to embed documents and create FAISS index
+def embed_and_index_documents(chunk_size=300):
+    global passages, embeddings, file_names, indexes, index
+    
+    passages, file_names, indexes = [], [], []
+    
+    for file_name, text in documents.items():
+        chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
+        passages.extend(chunks)
+        file_names.extend([file_name] * len(chunks))
+        indexes.extend(range(len(chunks)))
+    
+    # Create embeddings
+    embeddings = embedding_model.encode(passages, convert_to_tensor=False)
+    embedding_matrix = np.array(embeddings)
+    
+    # Build FAISS index
+    index = faiss.IndexFlatL2(embedding_matrix.shape[1])
+    index.add(embedding_matrix)
+    return "Documents embedded and indexed successfully."
+
+# Function to retrieve relevant passages
+def retrieve_relevant_passages(question, top_k=3):
+    question_embedding = embedding_model.encode([question])
+    distances, retrieved_indices = index.search(np.array(question_embedding), top_k)
+    retrieved_passages = [passages[i] for i in retrieved_indices[0]]
+    return retrieved_passages
+
+# Function to answer questions using retrieved passages
+def answer_question(question, top_k=3):
+    retrieved_passages = retrieve_relevant_passages(question, top_k)
+    context = " ".join(retrieved_passages)
+    input_text = f"Answer the question based on this content: {context}. Question: {question}"
+    
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    output_ids = qa_model.generate(input_ids, max_length=150)
+    answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+    return answer
+
+# Gradio interface functions
+def handle_file_upload(files):
+    message = upload_and_extract_text(files)
+    indexing_message = embed_and_index_documents()
+    return f"{message}\n{indexing_message}"
+
+def chat_with_pdfs(question):
+    answer = answer_question(question)
+    return answer
+
+# Define Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# PDF Chatbot using RAG (Retrieval-Augmented Generation)")
+    
+    with gr.Tab("Upload PDF(s)"):
+        file_upload = gr.File(label="Upload PDF files", file_types=[".pdf"], file_count="multiple")
+        upload_button = gr.Button("Process PDFs")
+        upload_output = gr.Textbox(label="Status")
+        upload_button.click(fn=handle_file_upload, inputs=file_upload, outputs=upload_output)
+    
+    with gr.Tab("Chat with PDFs"):
+        question_input = gr.Textbox(label="Ask a question about the uploaded PDFs")
+        answer_output = gr.Textbox(label="Answer")
+        question_input.submit(fn=chat_with_pdfs, inputs=question_input, outputs=answer_output)
+
+# Launch the app
+demo.launch()