Create app.py

app.py

@@ -0,0 +1,158 @@
+# app.py
+import gradio as gr
+from huggingface_hub import InferenceClient
+import os
+
+DEFAULT_MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"
+
+# Try both common environment variable names for Hugging Face tokens
+HF_TOKEN = os.getenv("API_TOKEN_2") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+client = None
+
+def get_inference_client(model_name):
+    global client
+    try:
+        if client is None or getattr(client, "model", None) != model_name:
+            client = InferenceClient(model=model_name, token=HF_TOKEN if HF_TOKEN else None)
+            print(f"InferenceClient initialized for {model_name}. Token {'provided' if HF_TOKEN else 'not provided'}.")
+    except Exception as e:
+        print(f"Failed to initialize InferenceClient for {model_name}: {e}")
+        return None
+    return client
+
+def evaluate_understanding(prompt, response):
+    if not response or response.strip() == "":
+        return "❌ Not Understood (Empty or whitespace response)"
+    response_lower = response.lower()
+    misunderstanding_keywords = [
+        "i'm sorry", "i apologize", "i cannot", "i am unable", "unable to",
+        "i don't understand", "could you please rephrase", "i'm not sure i follow",
+        "that's not clear", "i do not have enough information", "as an ai language model, i don't",
+        "i'm not programmed to", "i lack the ability to"
+    ]
+    for keyword in misunderstanding_keywords:
+        if keyword in response_lower:
+            return f"⚠️ Potentially Not Understood (Contains: '{keyword}')"
+    if len(prompt.split()) > 7 and len(response.split()) < 10:
+        return "⚠️ Potentially Not Understood (Response seems too short for the prompt)"
+    if prompt.lower() in response_lower and len(response_lower) < len(prompt.lower()) * 1.5:
+        if len(prompt.split()) > 5:
+            return "⚠️ Potentially Not Understood (Response might be echoing the prompt)"
+    return "✔️ Likely Understood"
+
+def query_model_and_evaluate(user_prompt, model_name_to_use):
+    if not user_prompt or user_prompt.strip() == "":
+        return "Please enter a prompt.", "Evaluation N/A", model_name_to_use
+    print(f"Querying model: {model_name_to_use}. HF_TOKEN {'is set' if HF_TOKEN else 'is NOT set/empty'}.")
+    current_client = get_inference_client(model_name_to_use)
+    if current_client is None:
+        error_msg = f"Error: Could not initialize the model API client for {model_name_to_use}. Check logs. This might be due to the model requiring authentication (like a token or accepting terms on Hugging Face) which was not available or successful."
+        return error_msg, "Evaluation N/A", model_name_to_use
+    try:
+        if "mistral" in model_name_to_use.lower() and "instruct" in model_name_to_use.lower():
+            formatted_prompt = f"<s>[INST] {user_prompt.strip()} [/INST]"
+        elif "llama-2" in model_name_to_use.lower() and "chat" in model_name_to_use.lower():
+            formatted_prompt = (
+                f"[INST] <<SYS>>\nYou are a helpful assistant. Your goal is to understand the user's prompt and respond accurately and relevantly.\n"
+                f"<</SYS>>\n\n{user_prompt.strip()} [/INST]"
+            )
+        else:
+            formatted_prompt = user_prompt.strip()
+        params = {
+            "max_new_tokens": 300,
+            "temperature": 0.6,
+            "top_p": 0.9,
+            "repetition_penalty": 1.1,
+            "do_sample": True,
+            "return_full_text": False
+        }
+        # Call the model
+        model_response_text = current_client.text_generation(formatted_prompt, **params)
+        if not model_response_text:
+            model_response_text = ""
+    except Exception as e:
+        error_message = f"Error calling model API for {model_name_to_use}: {str(e)}. This can happen if the model is gated, requires a Hugging Face token, or if you need to accept its terms of use on the Hugging Face website."
+        print(error_message)
+        return error_message, "Evaluation N/A", model_name_to_use
+    understanding_evaluation = evaluate_understanding(user_prompt, model_response_text)
+    return model_response_text, understanding_evaluation, model_name_to_use
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="orange")) as demo:
+    gr.Markdown(
+        f"""
+        # 🎯 Model Prompt Understanding Test
+        Enter a prompt for the selected language model. The application will send this to the model via Hugging Face's Inference API.
+        The model's response will be analyzed to provide a **basic heuristic assessment** of its understanding.
+        
+        **Selected Model:** <span id='current-model-display'>{DEFAULT_MODEL_NAME}</span>
+        """
+    )
+    current_model_name_state = gr.State(DEFAULT_MODEL_NAME)
+    with gr.Row():
+        user_input_prompt = gr.Textbox(
+            label="✏️ Enter your Prompt:",
+            placeholder="e.g., Explain the concept of zero-shot learning in 3 sentences.",
+            lines=4,
+            scale=3
+        )
+    submit_button = gr.Button("🚀 Submit Prompt and Evaluate", variant="primary")
+    gr.Markdown("---")
+    gr.Markdown("### 🤖 Model Response & Evaluation")
+    with gr.Row():
+        with gr.Column(scale=2):
+            model_output_response = gr.Textbox(
+                label="📝 Model's Response:", 
+                lines=10, 
+                interactive=False,
+                show_copy_button=True
+            )
+        with gr.Column(scale=1):
+            evaluation_output = gr.Textbox(
+                label="🧐 Understanding Evaluation:", 
+                lines=2, 
+                interactive=False,
+                show_copy_button=True
+            )
+            displayed_model = gr.Textbox(
+                label="⚙️ Model Used for this Response:",
+                interactive=False,
+                lines=1
+            )
+    submit_button.click(
+        fn=query_model_and_evaluate,
+        inputs=[user_input_prompt, current_model_name_state],
+        outputs=[model_output_response, evaluation_output, displayed_model]
+    )
+    gr.Markdown(
+        """
+        ---
+        **Disclaimer:**
+        * The 'Understanding Evaluation' is a very basic automated heuristic.
+        * **Using Models:** This app will attempt to connect to the selected model. Some models (especially gated ones like Llama-2) may require you to have a Hugging Face account, accept their terms of use on the Hugging Face website, and might implicitly require a valid `HF_TOKEN` associated with your account (even if not explicitly set as a secret in this Space). If a model call fails, it could be due to these reasons.
+        * Response quality depends heavily on the chosen model and the clarity of your prompt.
+        """
+    )
+    gr.Examples(
+        examples=[
+            ["Explain the difference between supervised and unsupervised machine learning.", DEFAULT_MODEL_NAME],
+            ["Write a short poem about a curious robot.", DEFAULT_MODEL_NAME],
+            ["What are the main challenges in developing AGI?", DEFAULT_MODEL_NAME],
+            ["Summarize the plot of 'War and Peace' in one paragraph.", DEFAULT_MODEL_NAME],
+            ["asdfjkl; qwerpoiu", DEFAULT_MODEL_NAME]
+        ],
+        inputs=[user_input_prompt, current_model_name_state],
+        outputs=[model_output_response, evaluation_output, displayed_model],
+        fn=query_model_and_evaluate,
+        cache_examples=False,
+        label="💡 Example Prompts (click to try)"
+    )
+
+if __name__ == "__main__":
+    print("Attempting to launch Gradio demo...")
+    print(f"Default model: {DEFAULT_MODEL_NAME}")
+    if HF_TOKEN:
+        print("HF_TOKEN is set.")
+    else:
+        print("HF_TOKEN is NOT set. Some models (especially gated ones like Llama) might require a token or prior agreement to terms on the Hugging Face website to function correctly. The app will attempt to run, but API calls may fail.")
+    demo.launch()