Update app.py

app.py

@@ -1,21 +1,27 @@
 import gradio as gr
-from transformers import pipeline
+from huggingface_hub import InferenceClient
 
-# Load the model
-model = pipeline("text-generation", model="meta-llama/Llama-2-7b-chat-hf")
+# Initialize the Hugging Face Inference client with the chosen model
+client = InferenceClient(repo_id="meta-llama/Llama-2-7b-chat-hf")
 
-def generate_response(prompt):
-    response = model(prompt, max_length=100, num_return_sequences=1)
-    return response[0]['generated_text']
+def evaluate_response(prompt):
+    # Send the prompt to the model and get the generated response
+    response = client.text_generation(prompt, max_new_tokens=200, temperature=0.7)
+    generated_text = response[0]['generated_text']
+    
+    # Simple quality evaluation: check length of the response
+    word_count = len(generated_text.split())
+    quality = "Good" if word_count > 10 else "Poor"
+    
+    return generated_text, f"Response Quality: {quality}"
 
-# Create the Gradio interface
+# Build the Gradio interface
 iface = gr.Interface(
-    fn=generate_response,
-    inputs=gr.Textbox(label="Enter your prompt or question:"),
-    outputs=gr.Textbox(label="Model Response:"),
-    title="Test Understanding Prompt",
-    description="Enter a prompt to see how well the model understands it."
+    fn=evaluate_response,
+    inputs=gr.Textbox(lines=4, label="Enter your prompt"),
+    outputs=[gr.Textbox(label="Model Response"), gr.Textbox(label="Quality Analysis")],
+    title="Model Understanding Test",
+    description="Enter instructions or a question, the app sends it to a language model and automatically evaluates the quality of the response."
 )
 
-# Launch the app
-iface.launch()
+iface.launch()