Update app.py

app.py

@@ -1,196 +1,144 @@
-import os
 import gradio as gr
-import requests
-import inspect
-import pandas as pd
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- 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
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+from textblob import TextBlob
+
+def analyze_text(text: str) -> str:
+    """Analyze text and return statistics.
+    
+    Args:
+        text: The input text to analyze
+    
+    Returns:
+        JSON string with analysis results
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    words = text.split()
+    chars = len(text)
+    chars_no_spaces = len(text.replace(" ", ""))
+    sentences = text.count(".") + text.count("!") + text.count("?")
+    
+    avg_word_length = round(chars_no_spaces / len(words), 2) if words else 0
+    avg_sentence_length = round(len(words) / max(sentences, 1), 2)
+    
+    return json.dumps({
+        "total_characters": chars,
+        "characters_without_spaces": chars_no_spaces,
+        "total_words": len(words),
+        "total_sentences": max(sentences, 1),
+        "average_word_length": avg_word_length,
+        "average_sentence_length": avg_sentence_length
+    }, indent=2)
+
+def extract_keywords(text: str, count: int = 5) -> str:
+    """Extract keywords (most common words) from text.
+    
+    Args:
+        text: The input text
+        count: Number of keywords to return (default 5)
+    
+    Returns:
+        JSON string with keywords and frequencies
     """
-    # --- 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}")
+    stopwords = {
+        "the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for",
+        "of", "with", "is", "are", "was", "were", "be", "been", "by", "from"
+    }
+    
+    words = text.lower().split()
+    filtered = [w.strip(".,!?;:") for w in words if w.lower() not in stopwords]
+    
+    from collections import Counter
+    word_freq = Counter(filtered)
+    top_words = word_freq.most_common(count)
+    
+    return json.dumps({
+        "keywords": [{"word": w, "frequency": f} for w, f in top_words]
+    }, indent=2)
+
+def check_reading_level(text: str) -> str:
+    """Estimate reading difficulty level.
+    
+    Args:
+        text: The input text
+    
+    Returns:
+        JSON string with reading level estimate
+    """
+    sentences = max(text.count(".") + text.count("!") + text.count("?"), 1)
+    words = len(text.split())
+    vowels = "aeiou"
+    syllables = sum(1 for c in text.lower() if c in vowels)
+    
+    if words == 0:
+        return json.dumps({"error": "No text to analyze"})
+    
+    grade = max(0, (0.39 * (words / sentences)) + (11.8 * (syllables / words)) - 15.59)
+    
+    if grade < 6:
+        level = "Elementary School"
+    elif grade < 9:
+        level = "Middle School"
+    elif grade < 13:
+        level = "High School"
     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
-        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})
-        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)
+        level = "College/Academic"
+    
+    return json.dumps({
+        "grade_level": round(grade, 1),
+        "reading_level": level
+    }, indent=2)
+
+def sentiment_analysis(text: str) -> dict:
+    """
+    Analyze the sentiment of the given text.
 
-    # 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)
+    Args:
+        text (str): The text to analyze
 
-    # 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.')}"
+    Returns:
+        dict: A dictionary containing polarity, subjectivity, and assessment
+    """
+    blob = TextBlob(text)
+    sentiment = blob.sentiment
+    
+    return {
+        "polarity": round(sentiment.polarity, 2),  # -1 (negative) to 1 (positive)
+        "subjectivity": round(sentiment.subjectivity, 2),  # 0 (objective) to 1 (subjective)
+        "assessment": "positive" if sentiment.polarity > 0 else "negative" if sentiment.polarity < 0 else "neutral"
+    }
+
+# Create web UI
+with gr.Blocks(title="Text Processor") as demo:
+    gr.Markdown("# Text Processing Tools")
+    gr.Markdown("Analyze text statistics, extract keywords, and check reading difficulty.")
+    
+    with gr.Tab("Analyze Text"):
+        text_input1 = gr.Textbox(
+            label="Enter text",
+            lines=8,
+            placeholder="Paste your text here..."
         )
-        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
-
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **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.
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    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]
-    )
-
+        analysis_output = gr.Textbox(label="Analysis Results", lines=8)
+        gr.Button("Analyze", size="lg").click(analyze_text, text_input1, analysis_output)
+    
+    with gr.Tab("Extract Keywords"):
+        text_input2 = gr.Textbox(label="Enter text", lines=8)
+        count_input = gr.Slider(1, 20, value=5, step=1, label="Number of keywords")
+        keywords_output = gr.Textbox(label="Keywords", lines=8)
+        gr.Button("Extract", size="lg").click(
+            extract_keywords,
+            [text_input2, count_input],
+            keywords_output
+        )
+    
+    with gr.Tab("Reading Level"):
+        text_input3 = gr.Textbox(label="Enter text", lines=8)
+        level_output = gr.Textbox(label="Reading Level Analysis", lines=5)
+        gr.Button("Check Level", size="lg").click(check_reading_level, text_input3, level_output)
+
+    with gr.Tab("Text Sentiment Analysis"):
+        text_input4 = gr.Textbox(label="Enter text", lines=8, placeholder="Enter text to analyze...")
+        sentiment_output = gr.Textbox(label="Text Sentiment Analysis", lines=5)
+        gr.Button("Check Level", size="lg").click(sentiment_analysis, text_input4, sentiment_output)
+
+# Launch the interface and MCP server
 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)
+    demo.launch(mcp_server=True)