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app.py

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+import faiss
+from sentence_transformers import SentenceTransformer
+import PyPDF2
+import gradio as gr
+
+# Step 1: Extract Knowledge Base from PDF
+def extract_text_from_pdf(pdf_path):
+    """
+    Extract and clean text from a PDF file.
+    """
+    knowledge = []
+    with open(pdf_path, 'rb') as file:
+        reader = PyPDF2.PdfReader(file)
+        for page in reader.pages:
+            text = page.extract_text()
+            if text:
+                # Replace newlines with spaces for better readability
+                text = text.replace("\n", " ")
+                # Split the text into meaningful sentences
+                knowledge.extend(text.split(". "))
+    # Clean up each sentence and remove empty strings
+    return [sentence.strip() for sentence in knowledge if sentence.strip()]
+
+# Step 2: Create the Retriever
+embedder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')  # Embedding model for document similarity
+
+index = None  # Global variable to store FAISS index
+knowledge_base = []  # Global variable to store the knowledge base
+
+def upload_and_query(pdf_file, query):
+    """
+    Handle PDF upload and process queries.
+    """
+    global index, knowledge_base
+
+    if not pdf_file:
+        return "Please upload a valid PDF file."
+
+    # Process the uploaded PDF
+    if not knowledge_base:  # Only process if the knowledge base is empty
+        knowledge_base = extract_text_from_pdf(pdf_file)
+        if not knowledge_base:
+            return "The uploaded PDF does not contain any readable text."
+
+        document_embeddings = embedder.encode(knowledge_base)
+        dimension = document_embeddings.shape[1]
+
+        index = faiss.IndexFlatL2(dimension)
+        index.add(document_embeddings)
+
+        return "PDF uploaded successfully. Now ask your query."
+
+    # Handle queries
+    if not query:
+        return "Please enter a query after uploading a PDF."
+
+    if index is None or not knowledge_base:
+        return "Please upload a PDF document before asking a query."
+
+    # Generate query embeddings and perform retrieval
+    query_embedding = embedder.encode([query])
+    distances, indices = index.search(query_embedding, 5)  # Retrieve top 5 results
+    retrieved_docs = [knowledge_base[idx] for idx in indices[0]]
+
+    # Clean up and format the retrieved context
+    context = " ".join(retrieved_docs).replace("\n", " ").strip()
+    return context
+
+# Step 3: Create Gradio Interface
+interface = gr.Interface(
+    fn=upload_and_query,
+    inputs=[
+        gr.File(label="Upload a PDF document", type="filepath"),
+        gr.Textbox(label="Enter your query"),
+    ],
+    outputs="text",
+    title="PDF Knowledge Base Query",
+    description="Upload a PDF document and ask questions based on its content."
+)
+
+# Launch the Interface
+if __name__ == "__main__":
+    interface.launch()

requirements

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+faiss-cpu
+sentence-transformers
+PyPDF2
+gradio