Update app.py

app.py

@@ -2,3 +2,50 @@ transformers
 torch
 sentence-transformers
 gradio
+
+import gradio as gr
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer, util
+
+# Load models
+retriever_model = SentenceTransformer('all-MiniLM-L6-v2')  # A sentence transformer for retrieval
+generator_model = pipeline('text-generation', model='gpt2')  # Use a suitable model
+
+# Sample document collection
+documents = [
+    "The quick fox jumps over the lazy dog.",
+    "A speedy brown fox jumps over the lazy dog.",
+    "A cat in the hat is wearing a red bow.",
+    "The sun sets behind the mountains, casting a warm glow."
+]
+
+# Function to perform retrieval and generation
+def rag_evaluation(query):
+    # Compute embeddings for the documents
+    doc_embeddings = retriever_model.encode(documents, convert_to_tensor=True)
+
+    # Compute embedding for the query
+    query_embedding = retriever_model.encode(query, convert_to_tensor=True)
+
+    # Calculate cosine similarity
+    similarities = util.pytorch_cos_sim(query_embedding, doc_embeddings)[0]
+
+    # Get the most similar document
+    most_similar_idx = similarities.argmax()
+    retrieved_doc = documents[most_similar_idx]
+
+    # Generate a response based on the retrieved document
+    generated_response = generator_model(f"Based on the document: {retrieved_doc}, answer: {query}", max_length=50)[0]['generated_text']
+
+    return retrieved_doc, generated_response
+
+# Gradio interface
+iface = gr.Interface(
+    fn=rag_evaluation,
+    inputs=gr.Textbox(label="Enter your query"),
+    outputs=[gr.Textbox(label="Retrieved Document"), gr.Textbox(label="Generated Response")],
+    title="RAG Evaluation App",
+    description="Evaluate retrieval and generation performance of a RAG system."
+)
+
+iface.launch()