Update app.py

app.py

@@ -1,25 +1,28 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the model
-model = pipeline("text-generation", model="meta-llama/Llama-2-7b-chat-hf")
+# Load a text generation pipeline from a Hugging Face model
+# You can replace this model with "meta-llama/Llama-2-7b-chat-hf" or "mistralai/Mistral-7B-Instruct" if available and compatible
+generator = pipeline("text-generation", model="meta-llama/Llama-2-7b-chat-hf")
 
-def generate_response(prompt):
-    response = model(prompt, max_length=100, num_return_sequences=1)
-    # Check if the response is not empty and contains the expected key
-    if response and isinstance(response, list) and 'generated_text' in response[0]:
-        return response[0]['generated_text']
-    else:
-        return "Error: No response generated."
+def evaluate_response(prompt):
+    # Generate text from the prompt
+    outputs = generator(prompt, max_new_tokens=150, do_sample=True, temperature=0.7)
+    generated_text = outputs[0]['generated_text']
+
+    # Simple quality evaluation: check length of generated 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
 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 evaluates the response quality."
 )
 
-# Launch the app
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()