Update app.py

app.py

@@ -27,131 +27,14 @@ from smolagents import DuckDuckGoSearchTool
 from smolagents.models import InferenceClientModel
 from smolagents import CodeAgent
 
-class CustomDuckDuckGoTool(DuckDuckGoSearchTool):
-    name = "duckduckgo_search"
-    description = "Searches the web using DuckDuckGo."
-    
-class WebSearchTool(Tool):
-    name = "web_search"
-    description = "Searches the web using DuckDuckGo and retrieves relevant answers."
-
-    def __init__(self):
-        self.agent = CodeAgent(
-            tools=[DuckDuckGoSearchTool()],
-            model=InferenceClientModel("deepseek-ai/DeepSeek-R1"),
-            name="WebSearcher",
-            description="Uses DuckDuckGo to answer queries with live web results.",
-            max_steps=5
-        )
-
-    def __call__(self, query: str) -> str:
-        try:
-            result = self.agent(query)
-            return result.get("output", "No response.")
-        except Exception as e:
-            return f"Web search failed: {e}"
-
-class VideoAnalyzerTool(Tool):
-    name = "video_analyzer"
-    description = "Analyzes frames from a video using image classification."
-
-    def __init__(self):
-        self.image_classifier = ImageClassifierTool()
-
-    def __call__(self, video_path: str) -> str:
-        cap = cv2.VideoCapture(video_path)
-        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        labels = set()
-
-        for i in range(0, frame_count, max(1, frame_count // 5)):
-            cap.set(cv2.CAP_PROP_POS_FRAMES, i)
-            ret, frame = cap.read()
-            if not ret:
-                continue
-            frame_path = f"temp_frame.jpg"
-            cv2.imwrite(frame_path, frame)
-            try:
-                label = self.image_classifier(frame_path)
-                labels.add(label)
-            except Exception as e:
-                labels.add(f"Error processing frame: {e}")
-            os.remove(frame_path)
-
-        cap.release()
-        return f"Video contains: {', '.join(labels)}"
-
-from smolagents import CodeAgent, Tool
-from PIL import Image
-import torch
-import torchvision.transforms as transforms
-from transformers import ViTForImageClassification, ViTFeatureExtractor
-import cv2
-import os
-
-class ImageClassifierTool(Tool):
-    name = "image_classifier"
-    description = "Classifies images using ViT."
-
-    def __init__(self):
-        self.model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
-        self.feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
-        self.transform = transforms.Compose([
-            transforms.Resize((224, 224)),
-            transforms.ToTensor()
-        ])
-        self.id2label = self.model.config.id2label
-
-    def __call__(self, image_path: str) -> str:
-        image = Image.open(image_path).convert("RGB")
-        inputs = self.feature_extractor(images=image, return_tensors="pt")
-        with torch.no_grad():
-            outputs = self.model(**inputs)
-        logits = outputs.logits
-        predicted_class_idx = logits.argmax(-1).item()
-        return f"Predicted label: {self.id2label[predicted_class_idx]}"
-
-class TimezoneTool(Tool):
-    name = "timezone_tool"
-    description = "Returns the current time for a given city."
-
-    def __call__(self, city: str) -> str:
-        url = f"http://worldtimeapi.org/api/timezone"
-        response = requests.get(url).json()
-        # You'd want to match city to a timezone
-        return "It's 9:45 AM in Tokyo."
-        
-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 ( modify this part to create your agent)
     try:
         agent = CodeAgent(
-    tools=[
-        ImageClassifierTool(),
-        VideoAnalyzerTool(),
-        TimezoneTool(),
-        WebSearchTool(),  # Now a Tool, so it can be integrated!
-    ],
+    tools=[DuckDuckGoSearchTool()],
     model=InferenceClientModel("HuggingFaceH4/zephyr-7b-beta"),
     max_steps=5,
-    name="web/media-agent",
-    description="An intelligent assistant that can classify images, summarize videos, check timezones, and search the web in real time."
+    verbosity_level=2        
 )
 
     except Exception as e: