Update app.py

app.py

@@ -12,7 +12,7 @@ import pdfplumber
 # ==== CONFIG ====
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 HF_TOKEN = os.getenv("HF_TOKEN")
-GROK_API_KEY = os.getenv("GROK_API_KEY") or "xai-AyJXz3OAAMuQiOrPzPptUWTmsEyI9vywPpbV19S1nCpXXKWoKLqOoGc61RazPPui2fx4Ekb1durXccqz"
+GROK_API_KEY = os.getenv("GROK_API_KEY") 
 
 CONVERSATIONAL_MODELS = [
     "deepseek-ai/DeepSeek-LLM",
@@ -25,7 +25,7 @@ wiki_api = wikipediaapi.Wikipedia(language="en", user_agent="SmartAgent/1.0 (cho
 # ==== UTILITY: Link/file detection ====
 def extract_links(text):
     url_pattern = re.compile(r'(https?://[^\s\)\],]+)')
-    return url_pattern.findall(text)
+    return url_pattern.findall(text or "")
 
 def download_file(url, out_dir="tmp_files"):
     os.makedirs(out_dir, exist_ok=True)
@@ -70,7 +70,7 @@ def analyze_file(file_path):
             return f"TXT error: {e}"
     else:
         return f"Unsupported file type: {file_path}"
-    
+
 def analyze_webpage(url):
     try:
         r = requests.get(url, timeout=15)
@@ -108,36 +108,40 @@ def llm_conversational(query):
             hf_client = InferenceClient(model_id, token=HF_TOKEN)
             # Try conversational if available, else fallback to text_generation
             if hasattr(hf_client, "conversational"):
-                result = hf_client.conversational(
-                    messages=[{"role": "user", "content": query}],
-                    max_new_tokens=384,
-                )
+                try:
+                    result = hf_client.conversational(
+                        messages=[{"role": "user", "content": query}],
+                        max_new_tokens=384,
+                    )
+                    if isinstance(result, dict) and "generated_text" in result:
+                        return result["generated_text"]
+                    elif hasattr(result, "generated_text"):
+                        return result.generated_text
+                    elif isinstance(result, str):
+                        return result
+                except Exception:
+                    pass
+            # Fallback to text_generation
+            try:
+                result = hf_client.text_generation(query, max_new_tokens=384)
                 if isinstance(result, dict) and "generated_text" in result:
                     return result["generated_text"]
-                elif hasattr(result, "generated_text"):
-                    return result.generated_text
                 elif isinstance(result, str):
                     return result
-                else:
-                    continue
-            result = hf_client.text_generation(query, max_new_tokens=384)
-            if isinstance(result, dict) and "generated_text" in result:
-                return result["generated_text"]
-            elif isinstance(result, str):
-                return result
+            except Exception:
+                pass
         except Exception as e:
             last_error = f"{model_id}: {e}"
     return None
 
 def is_coding_question(text):
-    # Basic heuristic: mentions code, function, "python", code blocks, etc.
     code_terms = [
         "python", "java", "c++", "code", "function", "write a", "script", "algorithm",
         "bug", "traceback", "error", "output", "compile", "debug"
     ]
-    if any(term in text.lower() for term in code_terms):
+    if any(term in (text or "").lower() for term in code_terms):
         return True
-    if re.search(r"```.+```", text, re.DOTALL):
+    if re.search(r"```.+```", text or "", re.DOTALL):
         return True
     return False
 
@@ -160,9 +164,8 @@ def grok_completion(question, system_prompt=None):
         r = requests.post(url, headers=headers, json=payload, timeout=45)
         r.raise_for_status()
         data = r.json()
-        # Extract assistant's reply
         return data['choices'][0]['message']['content']
-    except Exception as e:
+    except Exception:
         return None
 
 # ==== SMART AGENT ====
@@ -181,6 +184,8 @@ class SmartAgent:
                     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: