Update text_generator.py

text_generator.py

@@ -1,93 +1,68 @@
-import json
-import gradio as gr
-
-def generate_files(title="Text Generation Tool", emoji="🌖", colorFrom="blue", colorTo="blue",
-                   sdk="gradio", sdk_version="4.3.0", app_file="app.py", pinned=False,
-                   tags=["tool"], tool_name="text_generator", tool_description="This is a tool that chats with a user. "
-                   "It takes an input named `prompt` which contains a system_role, user_message, context and history. "
-                   "It returns a text message."):
-    # Generate readme content
-    readme_content = f'''## readme
-title: {title}
-emoji: {emoji}
-colorFrom: {colorFrom}
-colorTo: {colorTo}
-sdk: {sdk}
-sdk_version: {sdk_version}
-app_file: {app_file}
-pinned: {pinned}
-tags:
-  - {tags[0]}
-'''
-
-    # Generate tool config JSON content
-    tool_config = {
-        "description": tool_description,
-        "name": tool_name,
-        "tool_class": f"{tool_name.capitalize()}Tool"
-    }
-    tool_config_json = json.dumps(tool_config, indent=4)
-
-    # Generate app.py content
-    app_py_content = f'''from transformers.tools.base import launch_gradio_demo
-                    from {tool_name} import {tool_name.capitalize()}Tool
-                    
-                    launch_gradio_demo({tool_name.capitalize()}Tool)
-                    '''
-
-    # Generate requirements.txt content
-    requirements_content = '''transformers>=4.29.0
-            # diffusers
-            accelerate
-            torch
-            '''
-
-    # Generate text_generator.py content
-    text_generator_py_content = f'''import os
-    from transformers import pipeline
-    from transformers import Tool
-    
-    class {tool_name.capitalize()}Tool(Tool):
-        name = "{tool_name}"
-        description = (
-            "{tool_description}"
-        )
-    
-        inputs = ["text"]
-        outputs = ["text"]
-    
-        def __call__(self, prompt: str):
-            token = os.environ['hf']
-            text_generator = pipeline(model="microsoft/Orca-2-13b", token=token)
-            generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
-            print(generated_text)
-            return generated_text
-    '''
-
-    # Write content to files
-    with open("README.md", "w") as readme_file:
-        readme_file.write(readme_content)
-
-    with open("tool_config.json", "w") as tool_config_file:
-        tool_config_file.write(tool_config_json)
-
-    with open("app.py", "w") as app_py_file:
-        app_py_file.write(app_py_content)
-
-    with open("requirements.txt", "w") as requirements_file:
-        requirements_file.write(requirements_content)
-
-    with open(f"{tool_name}.py", "w") as text_generator_py_file:
-        text_generator_py_file.write(text_generator_py_content)
-
-    # Return the generated files for download
-    return "README.md", "tool_config.json", "app.py", "requirements.txt", f"{tool_name}.py"
-
-
-# Define the inputs for the Gradio interface
-io = gr.Interface(generate_files, 
-                  inputs=["text", "text", "text", "text", "text", "text", "text", "text", "checkbox", "text", "text"],
-                  outputs=["text", "text", "text", "text", "text"])
-
-# Launch the Gradio interface
-io.launch()
+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_token']
+        #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)