Upload tools.py

tools.py

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+from smolagents import DuckDuckGoSearchTool
+from smolagents import Tool
+from huggingface_hub import InferenceClient
+
+class Web_research(Tool):
+    name="web_research"
+    #description="You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don’t use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don’t use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."
+    description = "Web search on a specific topic."
+    inputs = {
+        "topic": {
+            "type": "string",
+            "description": "The topic on which the user wants the latest news"
+        }
+    }
+    output_type = "string"
+
+    def forward(self, topic: str):
+        search_tool = DuckDuckGoSearchTool()
+        # Example usage
+        results = search_tool(f"{topic}")
+        return f"Here is what we can find on the web for {topic} : str({results})"
+
+class image_interpreter(Tool):
+    name="multimodal_tool"
+    description = "Allows you to answer any question which relies on image input."
+    inputs = {
+        'image': {"type": "image", "description": "the image of interest"},
+        'prompt': {"type": "string", "description": "Any specific question you have on the image. For example, the prompt can be : Summarise this image in one sentence."}
+    }
+    output_type = "image"
+    output_type = "string"
+    model_sdxl = "trillionlabs/Trillion-LLaVA-7B"
+    client = InferenceClient(model_sdxl)
+    
+    def forward(self, prompt, image):
+        output = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": {image},
+                    },
+                    {
+                        "type": "text",
+                        "text": {prompt},
+                    },
+                ],
+            },
+        ],
+        )
+        return output
+