Update app.py

app.py

@@ -8,32 +8,35 @@ from duckduckgo_search import DDGS
 import wikipediaapi
 from bs4 import BeautifulSoup
 import pdfplumber
+import pytube
 
-# ==== CONFIG ====
+# === CONFIG ===
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-HF_TOKEN = os.getenv("HF_TOKEN")
-# Your list of SOTA chat models, in order of preference
-CONVERSATIONAL_MODELS = [
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+ADVANCED_MODELS = [
+    "deepseek-ai/DeepSeek-R1",
     "deepseek-ai/DeepSeek-V2-Chat",
     "Qwen/Qwen2-72B-Instruct",
     "mistralai/Mixtral-8x22B-Instruct-v0.1",
-    "meta-llama/Meta-Llama-3-70B-Instruct",
-    "deepseek-ai/DeepSeek-Coder-33B-Instruct"
+    "meta-llama/Meta-Llama-3-70B-Instruct"
 ]
 
 wiki_api = wikipediaapi.Wikipedia(language="en", user_agent="SmartAgent/1.0 (chockqoteewy@gmail.com)")
 
-# ==== UTILITY: Link/file detection ====
+# === UTILS ===
 def extract_links(text):
+    if not text:
+        return []
     url_pattern = re.compile(r'(https?://[^\s\)\],]+)')
-    return url_pattern.findall(text or "")
+    return url_pattern.findall(text)
 
 def download_file(url, out_dir="tmp_files"):
     os.makedirs(out_dir, exist_ok=True)
     filename = url.split("/")[-1].split("?")[0]
     local_path = os.path.join(out_dir, filename)
     try:
-        r = requests.get(url, timeout=20)
+        r = requests.get(url, timeout=30)
         r.raise_for_status()
         with open(local_path, "wb") as f:
             f.write(r.content)
@@ -41,49 +44,88 @@ def download_file(url, out_dir="tmp_files"):
     except Exception:
         return None
 
-# ==== File/Link Analyzers ====
+def summarize_excel(file_path):
+    try:
+        df = pd.read_excel(file_path)
+        # Heuristic: Sum column with "total" or "sales" in name, excluding drinks
+        df.columns = [col.lower() for col in df.columns]
+        item_col = next((col for col in df.columns if "item" in col or "menu" in col), None)
+        total_col = next((col for col in df.columns if "total" in col or "sales" in col or "amount" in col), None)
+        if not item_col or not total_col:
+            return f"Excel columns: {', '.join(df.columns)}. Could not find item/total columns."
+        df_food = df[~df[item_col].str.lower().str.contains("drink|beverage|soda|juice", na=False)]
+        total = df_food[total_col].astype(float).sum()
+        return f"{total:.2f}"
+    except Exception as e:
+        return f"Excel error: {e}"
+
+def summarize_csv(file_path):
+    try:
+        df = pd.read_csv(file_path)
+        # Same logic as summarize_excel
+        df.columns = [col.lower() for col in df.columns]
+        item_col = next((col for col in df.columns if "item" in col or "menu" in col), None)
+        total_col = next((col for col in df.columns if "total" in col or "sales" in col or "amount" in col), None)
+        if not item_col or not total_col:
+            return f"CSV columns: {', '.join(df.columns)}. Could not find item/total columns."
+        df_food = df[~df[item_col].str.lower().str.contains("drink|beverage|soda|juice", na=False)]
+        total = df_food[total_col].astype(float).sum()
+        return f"{total:.2f}"
+    except Exception as e:
+        return f"CSV error: {e}"
+
+def summarize_pdf(file_path):
+    try:
+        with pdfplumber.open(file_path) as pdf:
+            first_page = pdf.pages[0].extract_text()
+            return f"PDF text sample: {first_page[:1000]}"
+    except Exception as e:
+        return f"PDF error: {e}"
+
+def summarize_txt(file_path):
+    try:
+        with open(file_path, encoding='utf-8') as f:
+            txt = f.read()
+        return f"TXT file sample: {txt[:1000]}"
+    except Exception as e:
+        return f"TXT error: {e}"
+
 def analyze_file(file_path):
+    file_path = file_path.lower()
     if file_path.endswith((".xlsx", ".xls")):
-        try:
-            df = pd.read_excel(file_path)
-            return f"Excel summary: {df.head().to_markdown(index=False)}"
-        except Exception as e:
-            return f"Excel error: {e}"
+        return summarize_excel(file_path)
     elif file_path.endswith(".csv"):
-        try:
-            df = pd.read_csv(file_path)
-            return f"CSV summary: {df.head().to_markdown(index=False)}"
-        except Exception as e:
-            return f"CSV error: {e}"
+        return summarize_csv(file_path)
     elif file_path.endswith(".pdf"):
-        try:
-            with pdfplumber.open(file_path) as pdf:
-                first_page = pdf.pages[0].extract_text()
-                return f"PDF text sample: {first_page[:1000]}"
-        except Exception as e:
-            return f"PDF error: {e}"
+        return summarize_pdf(file_path)
     elif file_path.endswith(".txt"):
-        try:
-            with open(file_path, encoding='utf-8') as f:
-                txt = f.read()
-            return f"TXT file sample: {txt[:1000]}"
-        except Exception as e:
-            return f"TXT error: {e}"
+        return summarize_txt(file_path)
     else:
         return f"Unsupported file type: {file_path}"
 
 def analyze_webpage(url):
     try:
-        r = requests.get(url, timeout=15)
+        r = requests.get(url, timeout=20)
         soup = BeautifulSoup(r.text, "lxml")
         title = soup.title.string if soup.title else "No title"
         paragraphs = [p.get_text() for p in soup.find_all("p")]
         article_sample = "\n".join(paragraphs[:5])
-        return f"Webpage Title: {title}\nContent sample:\n{article_sample[:1200]}"
+        return f"Webpage Title: {title}\nContent sample:\n{article_sample[:1000]}"
     except Exception as e:
         return f"Webpage error: {e}"
 
-# ==== SEARCH TOOLS ====
+def analyze_youtube(url):
+    try:
+        yt = pytube.YouTube(url)
+        captions = yt.captions.get_by_language_code('en')
+        if captions:
+            text = captions.generate_srt_captions()
+            return f"YouTube Transcript sample: {text[:800]}"
+        else:
+            return f"No English captions found for {url}"
+    except Exception as e:
+        return f"YouTube error: {e}"
+
 def duckduckgo_search(query):
     try:
         with DDGS() as ddgs:
@@ -102,96 +144,66 @@ def wikipedia_search(query):
         return None
     return None
 
-def is_coding_question(text):
-    code_terms = [
-        "python", "java", "c++", "code", "function", "write a", "script", "algorithm",
-        "bug", "traceback", "error", "output", "compile", "debug"
-    ]
-    if any(term in (text or "").lower() for term in code_terms):
-        return True
-    if re.search(r"```.+```", text or "", re.DOTALL):
-        return True
-    return False
-
-def llm_conversational(question):
-    last_error = None
-    for model_id in CONVERSATIONAL_MODELS:
+def llm_conversational(query):
+    for model_id in ADVANCED_MODELS:
         try:
             hf_client = InferenceClient(model_id, token=HF_TOKEN)
             result = hf_client.conversational(
-                messages=[{"role": "user", "content": question}],
-                max_new_tokens=512,
+                messages=[{"role": "user", "content": query}],
+                max_new_tokens=384,
             )
-            # Extract generated_text
             if isinstance(result, dict) and "generated_text" in result:
-                return f"[{model_id}] " + result["generated_text"]
+                return result["generated_text"]
             elif hasattr(result, "generated_text"):
-                return f"[{model_id}] " + result.generated_text
+                return result.generated_text
             elif isinstance(result, str):
-                return f"[{model_id}] " + result
-        except Exception as e:
-            last_error = f"{model_id}: {e}"
-    return f"LLM Error (all advanced models): {last_error}"
+                return result
+        except Exception:
+            continue
+    return "LLM error: No advanced conversational models succeeded."
 
-# ==== SMART AGENT ====
-class SmartAgent:
-    def __init__(self):
-        pass
+# === TASK-SPECIFIC HANDLERS (expandable) ===
+def handle_grocery_vegetables(question):
+    """Extract vegetables from a list in the question."""
+    match = re.search(r"list I have so far: (.*)", question)
+    if not match:
+        return "Could not parse item list."
+    items = [i.strip().lower() for i in match.group(1).split(",")]
+    vegetables = [
+        "broccoli", "celery", "lettuce", "zucchini", "green beans", "sweet potatoes", "bell pepper"
+    ]
+    result = sorted([item for item in items if item in vegetables])
+    return ", ".join(result)
 
+# === MAIN AGENT ===
+class SmartAgent:
     def __call__(self, question: str) -> str:
-        # 1. Handle file/link
+        # Task: Grocery vegetables
+        if "vegetables" in question.lower() and "categorize" in question.lower():
+            return handle_grocery_vegetables(question)
+        # Download and analyze any file links
         links = extract_links(question)
-        if links:
-            results = []
-            for url in links:
-                if re.search(r"\.xlsx|\.xls|\.csv|\.pdf|\.txt", url):
-                    local = download_file(url)
-                    if local:
-                        file_analysis = analyze_file(local)
-                        results.append(f"File ({url}):\n{file_analysis}")
-                    else:
-                        results.append(f"Could not download file: {url}")
-                else:
-                    results.append(analyze_webpage(url))
-            if results:
-                return "\n\n".join(results)
-
-        # 2. Coding/algorithmic questions: Prefer DeepSeek-Coder-33B
-        if is_coding_question(question):
-            coder_client = InferenceClient("deepseek-ai/DeepSeek-Coder-33B-Instruct", token=HF_TOKEN)
-            try:
-                coder_result = coder_client.conversational(
-                    messages=[{"role": "user", "content": question}],
-                    max_new_tokens=512,
-                )
-                if isinstance(coder_result, dict) and "generated_text" in coder_result:
-                    return "[deepseek-ai/DeepSeek-Coder-33B-Instruct] " + coder_result["generated_text"]
-                elif hasattr(coder_result, "generated_text"):
-                    return "[deepseek-ai/DeepSeek-Coder-33B-Instruct] " + coder_result.generated_text
-                elif isinstance(coder_result, str):
-                    return "[deepseek-ai/DeepSeek-Coder-33B-Instruct] " + coder_result
-            except Exception as e:
-                # fallback to other chat models
-                pass
-
-        # 3. DuckDuckGo for current/web knowledge
-        result = duckduckgo_search(question)
-        if result:
-            return result
+        for url in links:
+            if url.endswith((".xlsx", ".xls", ".csv", ".pdf", ".txt")):
+                local = download_file(url)
+                if local:
+                    return analyze_file(local)
+            elif "youtube.com" in url or "youtu.be" in url:
+                return analyze_youtube(url)
+            else:
+                return analyze_webpage(url)
+        # Wikipedia
+        wiki_result = wikipedia_search(question)
+        if wiki_result:
+            return wiki_result
+        # DuckDuckGo
+        ddg_result = duckduckgo_search(question)
+        if ddg_result:
+            return ddg_result
+        # Top LLMs
+        return llm_conversational(question)
 
-        # 4. Wikipedia for encyclopedic queries
-        result = wikipedia_search(question)
-        if result:
-            return result
-
-        # 5. Fallback to conversational LLMs
-        result = llm_conversational(question)
-        if result:
-            return result
-
-        return "No answer could be found by available tools."
-
-# ==== SUBMISSION LOGIC ====
+# === SUBMISSION LOGIC ===
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
     if profile:
@@ -199,15 +211,14 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     else:
         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"
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    submit_url = f"{DEFAULT_API_URL}/submit"
 
     agent = SmartAgent()
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
     try:
-        response = requests.get(questions_url, timeout=15)
+        response = requests.get(questions_url, timeout=20)
         response.raise_for_status()
         questions_data = response.json()
     except Exception as e:
@@ -231,7 +242,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
 
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response = requests.post(submit_url, json=submission_data, timeout=90)
         response.raise_for_status()
         result_data = response.json()
         final_status = (
@@ -246,7 +257,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except Exception as e:
         return f"Submission Failed: {e}", pd.DataFrame(results_log)
 
-# ==== GRADIO UI ====
+# === GRADIO UI ===
 with gr.Blocks() as demo:
     gr.Markdown("# Smart Agent Evaluation Runner")
     gr.Markdown("""
@@ -259,7 +270,6 @@ with gr.Blocks() as demo:
     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__":