Update app.py

app.py

@@ -1,158 +1,33 @@
-# app.py
 import gradio as gr
 from huggingface_hub import InferenceClient
 import os
 
-DEFAULT_MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"
+HF_TOKEN = os.getenv("API_TOKEN_2")
 
-# Try both common environment variable names for Hugging Face tokens
-HF_TOKEN = os.getenv("API_TOKEN_2") 
-print(f"HF_TOKEN is {'set' if HF_TOKEN else 'NOT set'}.")
-
-client = None
-
-def get_inference_client(model_name):
-    global client
-    if client is None or getattr(client, "model", None) != model_name:
-        try:
-            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
+def query_model(prompt):
+    if not prompt or not prompt.strip():
+        return "Please enter a prompt."
     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
-        }
-        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
+        client = InferenceClient(model=MODEL_NAME, token=HF_TOKEN)
+        formatted_prompt = f"<s>[INST] {prompt.strip()} [/INST]"
+        response = client.text_generation(
+            formatted_prompt,
+            max_new_tokens=300,
+            temperature=0.6,
+            top_p=0.9,
+            repetition_penalty=1.1,
+            do_sample=True,
+            return_full_text=False
         )
-    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()
+        return response if response else "(No response from model.)"
+    except Exception as e:
+        return f"Error: {str(e)}\n\nThis can happen if the model is gated, requires a Hugging Face token, or you need to accept its terms of use on the Hugging Face website."
+
+gr.Interface(
+    fn=query_model,
+    inputs=gr.Textbox(lines=4, label="Enter your prompt:"),
+    outputs=gr.Textbox(lines=10, label="Model Response"),
+    title="Simple Mistral-7B-Instruct Demo",
+    description="Enter a prompt and get a response from mistralai/Mistral-7B-Instruct-v0.1."
+).launch()