Delete abc.txt

abc.txt

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-import gradio as gr
-import openai
-import base64
-from PIL import Image
-import io
-
-# Function to send the request to OpenAI API with an image or text input
-def generate_response(input_text, image, openai_api_key, reasoning_effort="medium", model_choice="o1"):
-    if not openai_api_key:
-        return "Error: No API key provided."
-
-    openai.api_key = openai_api_key
-
-    # Process the input depending on whether it's text or an image
-    if image:
-        # Convert the image to base64 string
-        image_info = get_base64_string_from_image(image)
-        input_text = f"data:image/png;base64,{image_info}"
-
-    # Prepare the messages for OpenAI API
-    if model_choice == "o1":
-        messages = [
-            {"role": "user", "content": [{"type": "image_url", "image_url": {"url": input_text}}]}
-        ]
-    elif model_choice == "o3-mini":
-        messages = [
-            {"role": "user", "content": [{"type": "text", "text": input_text}]}
-        ]
-    
-    try:
-        # Call OpenAI API with the selected model
-        response = openai.ChatCompletion.create(
-            model=model_choice,  # Dynamically choose the model (o1 or o3-mini)
-            messages=messages,
-            reasoning_effort=reasoning_effort,  # Set reasoning_effort for the response
-            max_completion_tokens=2000  # Limit response tokens to 2000
-        )
-
-        return response["choices"][0]["message"]["content"]
-    except Exception as e:
-        return f"Error calling OpenAI API: {str(e)}"
-
-# Function to convert an uploaded image to a base64 string
-def get_base64_string_from_image(pil_image):
-    # Convert PIL Image to bytes
-    buffered = io.BytesIO()
-    pil_image.save(buffered, format="PNG")
-    img_bytes = buffered.getvalue()
-    base64_str = base64.b64encode(img_bytes).decode("utf-8")
-    return base64_str
-
-# The function that will be used by Gradio interface
-def chatbot(input_text, image, openai_api_key, reasoning_effort, model_choice, history=[]):
-    response = generate_response(input_text, image, openai_api_key, reasoning_effort, model_choice)
-    
-    # Append the response to the history
-    history.append((f"User: {input_text}", f"Assistant: {response}"))
-    
-    return "", history
-
-# Function to clear the chat history
-def clear_history():
-    return "", []
-
-# Gradio interface setup
-def create_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("# Multimodal Chatbot (Text + Image)")
-
-        # Add a description after the title
-        gr.Markdown("""
-        ### Description:
-        This is a multimodal chatbot that can handle both text and image inputs. 
-        - You can ask questions or provide text, and the assistant will respond.
-        - You can also upload an image, and the assistant will process it and answer questions about the image.
-        - Enter your OpenAI API key to start interacting with the model.
-        - You can use the 'Clear History' button to remove the conversation history.
-        - "o1" is for image chat and "o3-mini" is for text chat.
-        ### Reasoning Effort:
-        The reasoning effort controls how complex or detailed the assistant's answers should be. 
-        - **Low**: Provides quick, concise answers with minimal reasoning or details.
-        - **Medium**: Offers a balanced response with a reasonable level of detail and thought.
-        - **High**: Produces more detailed, analytical, or thoughtful responses, requiring deeper reasoning.
-        """)
-
-        with gr.Row():
-            openai_api_key = gr.Textbox(label="Enter OpenAI API Key", type="password", placeholder="sk-...", interactive=True)
-        
-        with gr.Row():
-            image_input = gr.Image(label="Upload an Image", type="pil")  # Image upload input
-            input_text = gr.Textbox(label="Enter Text Question", placeholder="Ask a question or provide text", lines=2)
-        
-        with gr.Row():
-            reasoning_effort = gr.Dropdown(
-                label="Reasoning Effort",
-                choices=["low", "medium", "high"],
-                value="medium"
-            )
-            model_choice = gr.Dropdown(
-                label="Select Model",
-                choices=["o1", "o3-mini"],
-                value="o1"  # Default to 'o1' for image-related tasks
-            )
-            submit_btn = gr.Button("Send")
-            clear_btn = gr.Button("Clear History")
-
-        chat_history = gr.Chatbot()
-
-        # Button interactions
-        submit_btn.click(fn=chatbot, inputs=[input_text, image_input, openai_api_key, reasoning_effort, model_choice, chat_history], outputs=[input_text, chat_history])
-        clear_btn.click(fn=clear_history, inputs=[], outputs=[chat_history, chat_history])
-
-    return demo
-
-# Run the interface
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.launch()