Create app.py

app.py

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+### Set Up the Language Model
+
+from transformers import pipeline
+
+# Load a pre-trained model
+language_model = pipeline("text-generation", model="gpt-2")
+
+### Index with LlamaIndex
+
+from llama_index import LlamaIndex
+
+# Initialize LlamaIndex
+index = LlamaIndex()
+
+# Add documents to the index
+documents = ["demo_data_for_RAG.pdf"]
+index.add_documents(documents)
+
+
+### Implement RAG Logic
+
+def retrieve_and_generate_answer(question):
+    # Retrieve relevant documents
+    retrieved_docs = index.retrieve(question)
+
+    # Generate answer using the language model
+    context = " ".join(retrieved_docs)
+    answer = language_model(context + " " + question, max_length=100)
+    return answer[0]['generated_text']
+
+
+### Gradio Interface
+
+import gradio as gr
+
+def answer_question(question):
+    return retrieve_and_generate_answer(question)
+
+# Create Gradio interface
+iface = gr.Interface(fn=answer_question, inputs="text", outputs="text", title="Contextual QA System")
+
+# Launch the interface
+iface.launch()