Rename text_generator.py to most_downloaeded_model.py

most_downloaeded_model.py

@@ -0,0 +1,36 @@
+from transformers import Tool
+from huggingface_hub import list_models
+
+class HFModelDownloadsTool(Tool):
+    name = "model_download_counter"
+    description = (
+        "This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. "
+        "It takes the name of the category (such as text-classification, depth-estimation, etc), and "
+        "returns the name of the checkpoint."
+    )
+
+    inputs = ["text"]
+    outputs = ["text"]
+
+    def __call__(self, task: str):
+        model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
+        return model.id
+
+# Push the tool to the Hub
+tool = HFModelDownloadsTool()
+tool.push_to_hub("hf-model-downloads")
+
+# Load the tool from the Hub
+loaded_tool = Tool.from_hub("hf-model-downloads")
+
+# Instantiate the HfAgent with the additional tool
+from transformers import HfAgent
+
+agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[loaded_tool])
+
+# Run the agent with the new tool
+result = agent.run(
+    "Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?"
+)
+
+print(result)

text_generator.py

@@ -1,68 +0,0 @@
-import requests
-import os
-from transformers import pipeline
-
-
-from transformers import Tool
-# Import other necessary libraries if needed
-
-class TextGenerationTool(Tool):
-    name = "text_generator"
-    description = (
-        "This is a tool for text generation. It takes a prompt as input and returns the generated text."
-    )
-
-    inputs = ["text"]
-    outputs = ["text"]
-
-    def __call__(self, prompt: str):
-        #API_URL = "https://api-inference.huggingface.co/models/openchat/openchat_3.5"
-        #headers = {"Authorization": "Bearer " +  os.environ['hf']}
-        token=os.environ['hf']
-        #payload = {
-        #    "inputs": prompt  # Adjust this based on your model's input format
-        #}
-
-        #payload = {
-        #            "inputs": "Can you please let us know more details about your ",
-        #        }
-        
-        #def query(payload):
-        #generated_text = requests.post(API_URL, headers=headers, json=payload).json()
-        #print(generated_text)
-        #return generated_text["text"]
-            
-        # Replace the following line with your text generation logic
-        #generated_text = f"Generated text based on the prompt: '{prompt}'"
-
-        # Initialize the text generation pipeline
-        #text_generator = pipeline(model="lgaalves/gpt2-dolly", token=token)
-        text_generator = pipeline(model="microsoft/Orca-2-13b", token=token)
-
-        # Generate text based on a prompt
-        generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
-
-        # Print the generated text
-        print(generated_text)
-
-
-
-        return generated_text
-        
-        # Define the payload for the request
-        #payload = {
-        #    "inputs": prompt  # Adjust this based on your model's input format
-        #}
-
-        # Make the request to the API
-        #generated_text = requests.post(API_URL, headers=headers, json=payload).json()
-
-        # Extract and return the generated text
-        #return generated_text["generated_text"]
-
-# Uncomment and customize the following lines based on your text generation needs
-# text_generator = pipeline(model="gpt2")
-# generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
-
-# Print the generated text if needed
-# print(generated_text)