Update app.py

app.py

@@ -1,196 +1,186 @@
+"""
+Minimal Gradio interface for a simple AI assistant without smolagents
+
+This is a standalone version that uses only Hugging Face Inference API directly.
+It creates a simple Gradio interface for text generation.
+"""
+
 import os
-import gradio as gr
+import sys
+import json
 import requests
-import inspect
-import pandas as pd
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+import gradio as gr
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+# Check if running in Hugging Face Spaces
+IS_HF_SPACES = os.environ.get("SPACE_ID") is not None
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+class MinimalAIAssistant:
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Minimal AI Assistant using Hugging Face Inference API directly
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
+    def __init__(self, api_key=None, model_id="mistralai/Mixtral-8x7B-Instruct-v0.1"):
+        """
+        Initialize the minimal AI assistant
+        
+        Args:
+            api_key: Hugging Face API key
+            model_id: Model ID to use
+        """
+        self.api_key = api_key or os.environ.get("HF_API_KEY", "")
+        self.model_id = model_id
+        self.api_url = f"https://api-inference.huggingface.co/models/{model_id}"
+        self.headers = {"Authorization": f"Bearer {self.api_key}"}
+        
+        # System prompt
+        self.system_prompt = """
+You are an advanced AI assistant designed to help with various tasks.
+You can answer questions, provide information, and assist with problem-solving.
+Always be helpful, accurate, and concise in your responses.
+"""
+
+    def query(self, prompt):
+        """
+        Query the model with a prompt
+        
+        Args:
+            prompt: User prompt
+            
+        Returns:
+            Model response
+        """
         try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+            # Format the prompt with system message
+            formatted_prompt = f"{self.system_prompt}\n\nUser: {prompt}\n\nAssistant:"
+            
+            # Prepare the payload
+            payload = {
+                "inputs": formatted_prompt,
+                "parameters": {
+                    "max_new_tokens": 1024,
+                    "temperature": 0.7,
+                    "top_p": 0.95,
+                    "do_sample": True
+                }
+            }
+            
+            # Make the API request
+            response = requests.post(self.api_url, headers=self.headers, json=payload)
+            
+            # Check for errors
+            if response.status_code != 200:
+                return f"Error: API returned status code {response.status_code}. {response.text}"
+            
+            # Parse the response
+            result = response.json()
+            
+            # Extract the generated text
+            if isinstance(result, list) and len(result) > 0:
+                generated_text = result[0].get("generated_text", "")
+                # Remove the prompt from the response
+                if generated_text.startswith(formatted_prompt):
+                    generated_text = generated_text[len(formatted_prompt):].strip()
+                return generated_text
+            else:
+                return "Error: Unexpected response format from API"
+        
         except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+            return f"Error querying model: {str(e)}"
 
 
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
+def create_gradio_interface():
+    """
+    Create a Gradio interface for the minimal AI assistant
+    
+    Returns:
+        Gradio interface
+    """
+    # Initialize the assistant
+    assistant = MinimalAIAssistant()
+    
+    def process_query(query, api_key=""):
         """
-        **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        Process a user query
+        
+        Args:
+            query: User query
+            api_key: Hugging Face API key (optional)
+            
+        Returns:
+            Assistant's response
         """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+        # Update API key if provided
+        if api_key:
+            assistant.api_key = api_key
+            assistant.headers = {"Authorization": f"Bearer {api_key}"}
+        
+        # Check if API key is set
+        if not assistant.api_key:
+            return "Error: No API key provided. Please enter your Hugging Face API key."
+        
+        # Process the query
+        return assistant.query(query)
+    
+    # Create the interface
+    with gr.Blocks(title="Minimal AI Assistant") as interface:
+        gr.Markdown("# Minimal AI Assistant")
+        gr.Markdown("""
+        This is a minimal AI assistant using the Hugging Face Inference API.
+        Enter your query below and the assistant will respond.
+        """)
+        
+        api_key_input = gr.Textbox(
+            label="Hugging Face API Key",
+            placeholder="Enter your Hugging Face API key here...",
+            type="password"
+        )
+        
+        query_input = gr.Textbox(
+            label="Your Query",
+            placeholder="Enter your query here...",
+            lines=3
+        )
+        
+        submit_button = gr.Button("Submit")
+        
+        response_output = gr.Textbox(
+            label="Assistant Response",
+            lines=15
+        )
+        
+        # Sample queries
+        gr.Markdown("### Sample Queries")
+        sample_queries = [
+            "What is the capital of France?",
+            "Explain the concept of machine learning in simple terms.",
+            "Write a short poem about artificial intelligence."
+        ]
+        
+        for query in sample_queries:
+            sample_button = gr.Button(f"Try: {query}")
+            sample_button.click(
+                fn=lambda q=query: q,
+                outputs=query_input
+            )
+        
+        # Set up event handlers
+        submit_button.click(
+            fn=process_query,
+            inputs=[query_input, api_key_input],
+            outputs=response_output
+        )
+        
+        # Add examples
+        gr.Examples(
+            examples=sample_queries,
+            inputs=query_input
+        )
+    
+    return interface
 
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+# Create and launch the interface
+interface = create_gradio_interface()
 
+# For Hugging Face Spaces
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    interface.launch()