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import gradio as gr
import openai
import fitz # PyMuPDF for PDF processing
import base64
# Variable to store API key
api_key = ""
# Function to update API key
def set_api_key(key):
global api_key
api_key = key
return "API Key Set Successfully!"
# Function to interact with OpenAI API
def query_openai(messages, temperature, top_p, max_output_tokens):
if not api_key:
return "Please enter your OpenAI API key first."
try:
openai.api_key = api_key # Set API key dynamically
# Ensure numeric values for OpenAI parameters
temperature = float(temperature) if temperature else 1.0
top_p = float(top_p) if top_p else 1.0
max_output_tokens = int(max_output_tokens) if max_output_tokens else 1024
response = openai.ChatCompletion.create(
model="gpt-4.5-preview",
messages=messages,
temperature=temperature,
top_p=top_p,
max_tokens=max_output_tokens
)
return response["choices"][0]["message"]["content"]
except Exception as e:
return f"Error: {str(e)}"
# Function to process image URL input
def image_url_chat(image_url, text_query, temperature, top_p, max_output_tokens):
if not image_url or not text_query:
return "Please provide an image URL and a query."
messages = [
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": image_url}}, # Corrected format
{"type": "text", "text": text_query}
]}
]
return query_openai(messages, temperature, top_p, max_output_tokens)
# Function to process text input
def text_chat(text_query, temperature, top_p, max_output_tokens):
if not text_query:
return "Please enter a query."
messages = [{"role": "user", "content": [{"type": "text", "text": text_query}]}]
return query_openai(messages, temperature, top_p, max_output_tokens)
# Function to process uploaded image input
def image_chat(image_file, text_query, temperature, top_p, max_output_tokens):
if image_file is None or not text_query:
return "Please upload an image and provide a query."
# Encode image as base64
with open(image_file, "rb") as img:
base64_image = base64.b64encode(img.read()).decode("utf-8")
image_data = f"data:image/jpeg;base64,{base64_image}"
messages = [
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": image_data}}, # Fixed format
{"type": "text", "text": text_query}
]}
]
return query_openai(messages, temperature, top_p, max_output_tokens)
# Function to process uploaded PDF input
def pdf_chat(pdf_file, text_query, temperature, top_p, max_output_tokens):
if pdf_file is None or not text_query:
return "Please upload a PDF and provide a query."
# Extract text from the first few pages
doc = fitz.open(pdf_file)
text = "\n".join([page.get_text("text") for page in doc][:5]) # Limit extraction for performance
messages = [
{"role": "user", "content": [
{"type": "text", "text": text}, # Fixed format
{"type": "text", "text": text_query}
]}
]
return query_openai(messages, temperature, top_p, max_output_tokens)
# Function to clear the chat (Fix: Returns the correct number of outputs)
def clear_chat():
return "", "", "", "", "", "", "", None, "", None, "", 1.0, 1.0, 1024
# Gradio UI Layout
with gr.Blocks() as demo:
gr.Markdown("## GPT-4.5 Preview Chatbot")
# API Key Input
with gr.Row():
api_key_input = gr.Textbox(label="Enter OpenAI API Key", type="password")
api_key_button = gr.Button("Set API Key")
api_key_output = gr.Textbox(label="API Key Status", interactive=False)
with gr.Row():
temperature = gr.Slider(0, 2, value=1.0, step=0.1, label="Temperature")
top_p = gr.Slider(0, 1, value=1.0, step=0.1, label="Top-P")
max_output_tokens = gr.Slider(0, 16384, value=1024, step=512, label="Max Output Tokens")
with gr.Tabs():
with gr.Tab("Image URL Chat"):
image_url = gr.Textbox(label="Enter Image URL")
image_query = gr.Textbox(label="Ask about the Image")
image_url_output = gr.Textbox(label="Response", interactive=False)
image_url_button = gr.Button("Ask")
with gr.Tab("Text Chat"):
text_query = gr.Textbox(label="Enter your query")
text_output = gr.Textbox(label="Response", interactive=False)
text_button = gr.Button("Ask")
with gr.Tab("Image Chat"):
image_upload = gr.File(label="Upload an Image", type="filepath")
image_text_query = gr.Textbox(label="Ask about the uploaded image")
image_output = gr.Textbox(label="Response", interactive=False)
image_button = gr.Button("Ask")
with gr.Tab("PDF Chat"):
pdf_upload = gr.File(label="Upload a PDF", type="filepath")
pdf_text_query = gr.Textbox(label="Ask about the uploaded PDF")
pdf_output = gr.Textbox(label="Response", interactive=False)
pdf_button = gr.Button("Ask")
# Clear chat button
clear_button = gr.Button("Clear Chat")
# Button Click Actions
api_key_button.click(set_api_key, inputs=[api_key_input], outputs=[api_key_output])
image_url_button.click(image_url_chat, [image_url, image_query, temperature, top_p, max_output_tokens], image_url_output)
text_button.click(text_chat, [text_query, temperature, top_p, max_output_tokens], text_output)
image_button.click(image_chat, [image_upload, image_text_query, temperature, top_p, max_output_tokens], image_output)
pdf_button.click(pdf_chat, [pdf_upload, pdf_text_query, temperature, top_p, max_output_tokens], pdf_output)
# Fix: Clear button resets all necessary fields correctly
clear_button.click(
clear_chat,
outputs=[
image_url, image_query, image_url_output,
text_query, text_output,
image_text_query, image_output,
pdf_upload, pdf_text_query, pdf_output,
temperature, top_p, max_output_tokens
]
)
# Launch Gradio App
if __name__ == "__main__":
demo.launch() |