Update app.py

app.py

@@ -1,73 +1,258 @@
 import os
 import time
-import requests
 import moviepy
+import requests
 import whisper
 import gradio as gr
 import pandas as pd
-import wikipedia
 from duckduckgo_search import DDGS
 from transformers import pipeline
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
 
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-class SmartAgent:
+# --- Basic Agent Definition ---
+class BasicAgent:
     def __init__(self):
-        print("SmartAgent initialized.")
-        self.whisper_model = whisper.load_model("base")
-        self.qa_pipeline = pipeline("question-answering")
+        print("BasicAgent initialized.")
+        # Initialize the Whisper model for video transcription
+        self.whisper_model = whisper.load_model("base")  # You can change the model to `large`, `medium`, etc.
+        self.search_pipeline = pipeline("question-answering")
+        self.nlp_model = pipeline("feature-extraction")  # For semantic similarity (using transformer model)
+
+    def score_search_results(self, question: str, search_results: list) -> str:
+        # Transform the question and results to embeddings (vector representations)
+        question_embedding = self.nlp_model(question)
+        question_embedding = np.mean(question_embedding[0], axis=0)
+
+        best_score = -1
+        best_answer = None
+
+        # Loop through search results and calculate similarity
+        for result in search_results:
+            result_embedding = self.nlp_model(result['body'])
+            result_embedding = np.mean(result_embedding[0], axis=0)
+
+            # Calculate cosine similarity
+            similarity = cosine_similarity([question_embedding], [result_embedding])
+
+            # Check if this result is better
+            if similarity > best_score:
+                best_score = similarity
+                best_answer = result['body']
+
+        return best_answer
+
+    def search(self, question: str) -> str:
+        try:
+            with DDGS() as ddgs:
+                results = list(ddgs.text(question, max_results=3))  # Fetch top 3 results
+                if results:
+                    # Score all the results and return the best one
+                    return self.score_search_results(question, results)
+                else:
+                    return "No relevant search results found."
+        except Exception as e:
+            return f"Search error: {e}"
 
     def call_whisper(self, video_path: str) -> str:
+        # Transcribe the video to text using Whisper model
         video = moviepy.editor.VideoFileClip(video_path)
         audio_path = "temp_audio.wav"
         video.audio.write_audiofile(audio_path)
+        
+        # Transcribe audio to text
         result = self.whisper_model.transcribe(audio_path)
         return result["text"]
 
-    def classify_question(self, question: str) -> str:
-        q = question.lower()
-        if "how many" in q or "number of" in q:
-            return "count"
-        elif "when" in q or "what year" in q:
-            return "date"
-        return "open"
+    def __call__(self, question: str, video_path: str = None) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+
+        # If a video path is provided, use Whisper to transcribe the video
+        if video_path:
+            transcription = self.call_whisper(video_path)
+            print(f"Transcribed video text: {transcription[:100]}...")  # Print first 100 characters
+            return transcription
+
+        # If no video is provided, search the web for an answer
+        search_answer = self.search(question)
+        print(f"Agent returning search result: {search_answer[:100]}...")
+        time.sleep(2)
+        return search_answer
 
-    def wiki_search(self, question: str) -> str:
-        try:
-            results = wikipedia.search(question, results=1)
-            if not results:
-                return ""
-            page = wikipedia.page(results[0])
-            return page.content
-        except Exception as e:
-            return ""
 
-    def fallback_duckduckgo(self, question: str) -> str:
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    # --- 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
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    
+    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}")
+        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 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")
+        video_link = item.get("video_link")  # Assuming the question contains an optional video link
+
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+
         try:
-            with DDGS() as ddgs:
-                results = list(ddgs.text(question, max_results=3))
-                return results[0]["body"] if results else ""
+            # Pass video_link if available, else just the question text
+            submitted_answer = agent(question_text, video_path=video_link)
+            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:
-            return ""
+            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)
 
-    def answer_with_context(self, question: str, context: str) -> str:
+    # 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:
-            if not context.strip():
-                return "No reliable context found."
-            result = self.qa_pipeline(question=question, context=context)
-            return result["answer"]
-        except Exception as e:
-            return f"QA error: {e}"
+            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
 
-    def __call__(self, question: str, video_path: str = None) -> str:
-        print(f"Agent received question: {question[:60]}...")
 
-        if video_path:
-            return self.call_whisper(video_path)
+# --- 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.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+if __name__ == "__main__":
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")
 
-        classification = self.classify_question(question)
+    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.")
 
-        context = self.wiki_search(question)
-        if not context:
-            context = self.fallback_duckduckgo(question)
+    print("-"*(60 + len(" App Starting ")) + "\n")
 
-        return self.answer_with_context(question, context)
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)