Update tools.py

tools.py

@@ -1,57 +1,38 @@
-from smolagents import DuckDuckGoSearchTool
-from smolagents import Tool
-import random
-from huggingface_hub import list_models
-
-
-# Initialize the DuckDuckGo search tool
-#search_tool = DuckDuckGoSearchTool()
-
-
-class WeatherInfoTool(Tool):
-    name = "weather_info"
-    description = "Fetches dummy weather information for a given location."
-    inputs = {
-        "location": {
-            "type": "string",
-            "description": "The location to get weather information for."
-        }
-    }
-    output_type = "string"
-
-    def forward(self, location: str):
-        # Dummy weather data
-        weather_conditions = [
-            {"condition": "Rainy", "temp_c": 15},
-            {"condition": "Clear", "temp_c": 25},
-            {"condition": "Windy", "temp_c": 20}
-        ]
-        # Randomly select a weather condition
-        data = random.choice(weather_conditions)
-        return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"
-
-class HubStatsTool(Tool):
-    name = "hub_stats"
-    description = "Fetches the most downloaded model from a specific author on the Hugging Face Hub."
-    inputs = {
-        "author": {
-            "type": "string",
-            "description": "The username of the model author/organization to find models from."
-        }
-    }
-    output_type = "string"
-
-    def forward(self, author: str):
-        try:
-            # List models from the specified author, sorted by downloads
-            models = list(list_models(author=author, sort="downloads", direction=-1, limit=1))
-            
-            if models:
-                model = models[0]
-                return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
-            else:
-                return f"No models found for author {author}."
-        except Exception as e:
-            return f"Error fetching models for {author}: {str(e)}"
-
-
+from langchain.tools import DuckDuckGoSearchRun
+import pytesseract
+from PIL import Image
+import whisper
+
+
+# --- Web Search Tool ---
+def web_search_tool(question: str) -> str:
+    try:
+        return DuckDuckGoSearchRun().run(question)
+    except Exception as e:
+        return f"Web search failed: {str(e)}"
+
+# --- Image Analysis Tool ---
+def image_analysis_tool(image_path: str) -> str:
+    try:
+        image = Image.open(image_path)
+        text = pytesseract.image_to_string(image)
+        return f"Detected text in image:\n{text.strip()}"
+    except Exception as e:
+        return f"Image analysis failed: {str(e)}"
+
+# --- Audio Transcription Tool ---
+def audio_transcription_tool(audio_path: str) -> str:
+    try:
+        model = whisper.load_model("base")
+        result = model.transcribe(audio_path)
+        return result["text"].strip()
+    except Exception as e:
+        return f"Audio transcription failed: {str(e)}"
+
+# --- Document Tool ---
+def document_analysis_tool(text: str) -> str:
+    return f"Document contains {len(text.split())} words.\n\nSummary not yet implemented."
+
+# --- General LLM Logic Tool ---
+def llm_tool(question: str, llm) -> str:
+    return llm(f"Answer this question: {question}")