Create app.py

app.py

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+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import fitz  # PyMuPDF for PDF files
+from docx import Document
+from pptx import Presentation
+import gradio as gr
+
+# Initialize SentenceTransformer for embeddings
+retrieve = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# Initialize empty list for documents and embeddings
+documents = []
+doc_embeddings = []
+index = None  # FAISS index will be created only when documents are added
+
+# Function to process PDF files
+def process_pdf(file_path):
+    try:
+        doc = fitz.open(file_path)
+        text = ""
+        for page_num in range(doc.page_count):
+            text += doc[page_num].get_text()
+        return text
+    except Exception as e:
+        return f"Error reading PDF: {e}"
+
+# Function to process DOCX files
+def process_docx(file_path):
+    try:
+        doc = Document(file_path)
+        text = "\n".join([para.text for para in doc.paragraphs])
+        return text
+    except Exception as e:
+        return f"Error reading DOCX: {e}"
+
+# Function to process PPTX files
+def process_pptx(file_path):
+    try:
+        presentation = Presentation(file_path)
+        text = ""
+        for slide in presentation.slides:
+            for shape in slide.shapes:
+                if hasattr(shape, "text"):
+                    text += shape.text + "\n"
+        return text
+    except Exception as e:
+        return f"Error reading PPTX: {e}"
+
+# Function to add a document to the FAISS index
+def add_to_index(text):
+    global index, doc_embeddings, documents
+    if text.strip():  # Only add non-empty documents
+        embedding = retrieve.encode([text])[0]
+        doc_embeddings.append(embedding)
+        documents.append(text)
+        # Update FAISS index
+        embeddings_matrix = np.array(doc_embeddings)
+        index = faiss.IndexFlatL2(embeddings_matrix.shape[1])
+        index.add(embeddings_matrix)
+
+# Function to load and process a single document
+def load_document(file_path):
+    if file_path.endswith('.pdf'):
+        text = process_pdf(file_path)
+    elif file_path.endswith('.docx'):
+        text = process_docx(file_path)
+    elif file_path.endswith('.pptx'):
+        text = process_pptx(file_path)
+    else:
+        return "Unsupported file format"
+    
+    if isinstance(text, str) and "Error" not in text:
+        add_to_index(text)
+        return "Document loaded and indexed successfully."
+    return text  # Return error message if processing fails
+
+# Retrieve documents based on the query
+def retrieve_docs(query, k=2):
+    if not index:
+        return ["Index not initialized. Please upload and process a document first."]
+    query_embedding = retrieve.encode([query])
+    distances, indices = index.search(np.array(query_embedding), k)
+    results = [documents[i] for i in indices[0]]
+    return results
+
+# Generate a response based on retrieved documents
+def generate_response(retrieved_docs):
+    if retrieved_docs:
+        context = " ".join(retrieved_docs)
+        response = f"Generated response based on retrieved docs:\n\n{context[:500]}..."  # Placeholder response
+        return response
+    return "No relevant documents found to generate a response."
+
+# Gradio function
+def rag_application(query, file):
+    # Load and process the uploaded document if provided
+    if file:
+        load_result = load_document(file.name)
+        if "Error" in load_result:
+            return load_result, ""  # Return error message if document loading failed
+    
+    # Retrieve relevant documents
+    retrieved_docs = retrieve_docs(query)
+    docs_output = "\n".join([f"- {doc[:200]}..." for doc in retrieved_docs])  # Display snippets
+
+    # Generate response
+    response = generate_response(retrieved_docs)
+    return docs_output, response
+
+# Gradio interface
+iface = gr.Interface(
+    fn=rag_application,
+    inputs=[
+        "text",  # Query input
+        "file"   # Single file upload
+    ],
+    outputs=[
+        "text",  # Retrieved documents output
+        "text"   # Generated response output
+    ],
+    title="RAG Application with Single File Upload",
+    description="Upload a PDF, DOCX, or PPTX file and ask questions. The RAG application retrieves relevant documents and generates a response."
+)
+
+iface.launch()