Create app.py

app.py

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+import os
+os.environ["GROQ_API_KEY"] = "YOUR_GROQ_API_KEY"
+# from google.colab import userdata
+# GROQ_API_KEY=userdata.get('rag-based')
+import gradio as gr
+from groq import Groq
+
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+# -----------------------------
+# Environment Setup
+# -----------------------------
+
+# GROQ_API_KEY = os.environ.get("Rag-based")
+
+
+client = Groq(api_key=os.environ.get("Rag-based"))
+
+# -----------------------------
+# Global Variables
+# -----------------------------
+
+vector_db = None
+
+# -----------------------------
+# Embedding Model
+# -----------------------------
+
+embedding_model = HuggingFaceEmbeddings(
+    model_name="sentence-transformers/all-MiniLM-L6-v2"
+)
+
+# -----------------------------
+# Document Processing Function
+# -----------------------------
+
+def process_document(pdf_file):
+
+    global vector_db
+
+    if pdf_file is None:
+        return "Please upload a PDF Document first."
+
+    try:
+
+        # Load PDF
+        loader = PyPDFLoader(pdf_file.name)
+        documents = loader.load()
+
+        # Chunking
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=200
+        )
+
+        chunks = text_splitter.split_documents(documents)
+
+        # Create FAISS vector database
+        vector_db = FAISS.from_documents(
+            chunks,
+            embedding_model
+        )
+
+        return f"Document processed successfully. {len(chunks)} chunks of your document created. Now, proceed to ask your question ahead."
+
+    except Exception as e:
+        return f"Error processing document: {str(e)}"
+
+
+# -----------------------------
+# Question Answering Function
+# -----------------------------
+
+def ask_question(question):
+
+    global vector_db
+
+    if vector_db is None:
+        return "Please upload and process a PDF document first."
+
+    try:
+
+        # Retrieve relevant chunks
+        docs = vector_db.similarity_search(question, k=4)
+
+        context = "\n\n".join([doc.page_content for doc in docs])
+
+        prompt = f"""
+You are a helpful assistant. Answer the question ONLY using the following context.
+If the answer is not in the context, say "I could not find the answer in the provided context."
+
+Context:
+{context}
+
+Question:
+{question}
+
+Answer clearly and based only on the provided context.
+"""
+
+        # Groq LLM call
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {"role": "user", "content": prompt}
+            ],
+            model="llama-3.3-70b-versatile",
+        )
+
+        response = chat_completion.choices[0].message.content
+
+        return response
+
+    except Exception as e:
+        return f"Error generating answer: {str(e)}"
+
+
+# -----------------------------
+# Gradio Interface
+# -----------------------------
+
+with gr.Blocks() as demo:
+
+    gr.Markdown("# 📄 PDF Document Assistant Developed by Asif Jamal")
+
+    gr.Markdown(
+        "Upload a PDF document and ask questions about its content."
+    )
+
+    pdf_input = gr.File(label="Upload PDF Document")
+
+    process_button = gr.Button("Click to Process Document")
+
+    process_output = gr.Textbox(label="Processing Status")
+
+    process_button.click(
+        process_document,
+        inputs=pdf_input,
+        outputs=process_output
+    )
+
+    gr.Markdown("## Ask Questions")
+
+    question_input = gr.Textbox(
+        label="Enter your question."
+    )
+
+    ask_button = gr.Button("Click to Proceed")
+
+    answer_output = gr.Textbox(
+        label="Answer",
+        lines=10
+    )
+
+    ask_button.click(
+        ask_question,
+        inputs=question_input,
+        outputs=answer_output
+    )
+
+demo.launch()