Delete abc2.txt

abc2.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":
-        if image:
-            messages = [
-                {"role": "user", "content": [{"type": "image_url", "image_url": {"url": input_text}}]}
-            ]
-        else:
-            messages = [
-                {"role": "user", "content": [{"type": "text", "text": 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
-
-# Function to transcribe audio to text using OpenAI Whisper API
-def transcribe_audio(audio, openai_api_key):
-    if not openai_api_key:
-        return "Error: No API key provided."
-    
-    openai.api_key = openai_api_key
-    
-    try:
-        # Open the audio file and pass it as a file object
-        with open(audio, 'rb') as audio_file:
-            audio_file_content = audio_file.read()
-        
-        # Use the correct transcription API call
-        audio_file_obj = io.BytesIO(audio_file_content)
-        audio_file_obj.name = 'audio.wav'  # Set a name for the file object (as OpenAI expects it)
-
-        # Transcribe the audio to text using OpenAI's whisper model
-        audio_file_transcription = openai.Audio.transcribe(file=audio_file_obj, model="whisper-1")
-        return audio_file_transcription['text']
-    except Exception as e:
-        return f"Error transcribing audio: {str(e)}"
-
-# The function that will be used by Gradio interface
-def chatbot(input_text, image, audio, openai_api_key, reasoning_effort, model_choice, history=[]):
-    # If there's audio, transcribe it to text
-    if audio:
-        input_text = transcribe_audio(audio, openai_api_key)
-
-    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 "", []
-
-# Custom CSS styles with animations and button colors
-custom_css = """
-    /* General body styles */
-    .gradio-container {
-        font-family: 'Arial', sans-serif;
-        background-color: #f8f9fa;
-        color: #333;
-    }
-    /* Header styles */
-    .gradio-header {
-        background-color: #007bff;
-        color: white;
-        padding: 20px;
-        text-align: center;
-        border-radius: 8px;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-        animation: fadeIn 1s ease-out;
-    }
-    .gradio-header h1 {
-        font-size: 2.5rem;
-    }
-    .gradio-header h3 {
-        font-size: 1.2rem;
-        margin-top: 10px;
-    }
-    /* Chatbot container styles */
-    .gradio-chatbot {
-        background-color: #fff;
-        border-radius: 10px;
-        padding: 20px;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-        max-height: 500px;
-        overflow-y: auto;
-        animation: fadeIn 2s ease-out;
-    }
-    /* Input field styles */
-    .gradio-textbox, .gradio-dropdown, .gradio-image, .gradio-audio {
-        border-radius: 8px;
-        border: 2px solid #ccc;
-        padding: 10px;
-        margin-bottom: 10px;
-        width: 100%;
-        font-size: 1rem;
-        transition: all 0.3s ease;
-    }
-    .gradio-textbox:focus, .gradio-dropdown:focus, .gradio-image:focus, .gradio-audio:focus {
-        border-color: #007bff;
-    }
-    /* Button styles */
-    /* Send Button: Sky Blue */
-    #submit-btn {
-        background-color: #00aaff; /* Sky blue */
-        color: white;
-        border: none;
-        border-radius: 8px;
-        padding: 10px 19px;
-        font-size: 1.1rem;
-        cursor: pointer;
-        transition: all 0.3s ease;
-        margin-left: auto;
-        margin-right: auto;
-        display: block;
-        margin-top: 10px;
-    }
-    #submit-btn:hover {
-        background-color: #0099cc; /* Slightly darker blue */
-    }
-    #submit-btn:active {
-        transform: scale(0.95);
-    }
-    #clear-history {
-        background-color: #f04e4e; /* Slightly Darker red */
-        color: white;
-        border: none;
-        border-radius: 8px;
-        padding: 10px 13px;
-        font-size: 1.1rem;
-        cursor: pointer;
-        transition: all 0.3s ease;
-        margin-top: 10px;
-    }
-    #clear-history:hover {
-        background-color: #f5a4a4; /* Light red */
-    }
-    #clear-history:active {
-        transform: scale(0.95);
-    }
-    /* Chat history styles */
-    .gradio-chatbot .message {
-        margin-bottom: 10px;
-    }
-    .gradio-chatbot .user {
-        background-color: #007bff;
-        color: white;
-        padding: 10px;
-        border-radius: 12px;
-        max-width: 70%;
-        animation: slideInUser 0.5s ease-out;
-    }
-    .gradio-chatbot .assistant {
-        background-color: #f1f1f1;
-        color: #333;
-        padding: 10px;
-        border-radius: 12px;
-        max-width: 70%;
-        margin-left: auto;
-        animation: slideInAssistant 0.5s ease-out;
-    }
-    /* Animation keyframes */
-    @keyframes fadeIn {
-        0% { opacity: 0; }
-        100% { opacity: 1; }
-    }
-    @keyframes slideInUser {
-        0% { transform: translateX(-100%); }
-        100% { transform: translateX(0); }
-    }
-    @keyframes slideInAssistant {
-        0% { transform: translateX(100%); }
-        100% { transform: translateX(0); }
-    }
-    /* Mobile responsiveness */
-    @media (max-width: 768px) {
-        .gradio-header h1 {
-            font-size: 1.8rem;
-        }
-        .gradio-header h3 {
-            font-size: 1rem;
-        }
-        .gradio-chatbot {
-            max-height: 400px;
-        }
-        .gradio-textbox, .gradio-dropdown, .gradio-image, .gradio-audio {
-            width: 100%;
-        }
-        #submit-btn, #clear-history {
-            width: 100%;
-            margin-left: 0;
-        }
-    }
-"""
-
-# Gradio interface setup
-def create_interface():
-    with gr.Blocks(css=custom_css) as demo:
-        gr.Markdown("""
-            <div class="gradio-header">
-                <h1>Multimodal Chatbot (Text + Image + Voice)</h1>
-                <h3>Interact with a chatbot using text, image, or voice inputs</h3>
-            </div>
-        """)
-
-        # Add a description with an expandable accordion
-        with gr.Accordion("Click to expand for details", open=False):
-            gr.Markdown("""
-            ### Description:
-            This is a multimodal chatbot that can handle text, image, and voice 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.
-            - Voice input is supported: You can upload or record an audio file, and it will be transcribed to text and sent to the assistant.
-            - 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)
-            audio_input = gr.Audio(label="Upload or Record Audio", type="filepath")  # Audio upload or record input (using filepath)
-
-        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("Ask!", elem_id="submit-btn")
-            clear_btn = gr.Button("Clear History", elem_id="clear-history")
-
-        chat_history = gr.Chatbot()
-
-        # Button interactions
-        submit_btn.click(fn=chatbot, inputs=[input_text, image_input, audio_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()