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app.py

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+import os
+import gradio as gr
+from pypdf import PdfReader
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+from groq import Groq
+
+# -----------------------
+# Initialize embedding model
+# -----------------------
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# -----------------------
+# Initialize Groq client
+# -----------------------
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# -----------------------
+# Helper functions
+# -----------------------
+def extract_text_from_pdfs(pdf_files):
+    text = ""
+    for pdf in pdf_files:
+        reader = PdfReader(pdf)
+        for page in reader.pages:
+            text += page.extract_text() + "\n"
+    return text
+
+def chunk_text(text, chunk_size=500, overlap=100):
+    words = text.split()
+    chunks = []
+    i = 0
+    while i < len(words):
+        chunk = words[i:i + chunk_size]
+        chunks.append(" ".join(chunk))
+        i += chunk_size - overlap
+    return chunks
+
+def retrieve_chunks(pdf_files, question):
+    if not pdf_files:
+        return "❌ Please upload PDF files."
+    if not question:
+        return "❌ Please enter a question."
+
+    text = extract_text_from_pdfs(pdf_files)
+    chunks = chunk_text(text)
+
+    chunk_embeddings = model.encode(chunks)
+    question_embedding = model.encode([question])
+
+    scores = cosine_similarity(question_embedding, chunk_embeddings)[0]
+    top_indices = np.argsort(scores)[-3:][::-1]
+
+    retrieved_chunks = [chunks[i] for i in top_indices]
+    return retrieved_chunks
+
+# -----------------------
+# RAG + Groq LLM integration
+# -----------------------
+def answer_question(pdf_files, question, history):
+    retrieved_chunks = retrieve_chunks(pdf_files, question)
+    if isinstance(retrieved_chunks, str):
+        return retrieved_chunks, history
+
+    context = "\n\n".join(retrieved_chunks)
+    prompt = f"Context: {context}\n\nQuestion: {question}\nAnswer concisely:"
+
+    response = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ],
+        model="llama-3.1-8b-instant",
+        max_tokens=300
+    )
+
+    answer = response.choices[0].message.content
+
+    # Update history
+    history = history or ""
+    history += f"Q: {question}\nA: {answer}\n\n"
+    return answer, history
+
+# -----------------------
+# PDF Summarization
+# -----------------------
+def summarize_pdf(pdf_files):
+    if not pdf_files:
+        return "❌ Please upload PDF files first."
+
+    text = extract_text_from_pdfs(pdf_files)
+    chunks = chunk_text(text)
+    context = "\n\n".join(chunks[:5])  # summarize first 5 chunks for speed
+
+    prompt = f"Summarize the following PDF content concisely:\n\n{context}"
+
+    response = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": "You are a helpful summarizer."},
+            {"role": "user", "content": prompt}
+        ],
+        model="llama-3.1-8b-instant",
+        max_tokens=200
+    )
+
+    summary = response.choices[0].message.content
+    return summary
+
+# -----------------------
+# Gradio UI
+# -----------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 RAG PDF Chatbot with History & PDF Summarization")
+
+    pdf_input = gr.File(
+        label="Upload PDF Files",
+        file_types=[".pdf"],
+        file_count="multiple"
+    )
+
+    question_input = gr.Textbox(
+        label="Ask your question here",
+        placeholder="e.g. What is the main objective of this document?"
+    )
+
+    history_box = gr.Textbox(
+        label="Answer History",
+        lines=10,
+        interactive=False
+    )
+
+    answer_box = gr.Textbox(
+        label="Answer",
+        lines=8
+    )
+
+    # Buttons
+    get_answer_btn = gr.Button("Get Answer")
+    summarize_btn = gr.Button("Summarize PDF")
+
+    # Button actions
+    get_answer_btn.click(
+        fn=answer_question,
+        inputs=[pdf_input, question_input, history_box],
+        outputs=[answer_box, history_box]
+    )
+
+    summarize_btn.click(
+        fn=summarize_pdf,
+        inputs=[pdf_input],
+        outputs=[answer_box]
+    )
+
+demo.launch()

requirments.txt

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+gradio
+groq
+pypdf
+sentence-transformers
+scikit-learn
+numpy