Create app.py

app.py

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+import gradio as gr
+import openai
+import fitz  # PyMuPDF for PDF processing
+import base64
+
+# Variable to store the API key
+api_key = ""
+
+# Function to update the 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
+        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):
+    messages = [
+        {"role": "user", "content": [{"type": "input_image", "image_url": image_url}, {"type": "input_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):
+    messages = [{"role": "user", "content": 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:
+        return "Please upload an image."
+
+    # 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": "input_image", "image_url": image_data}, {"type": "input_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:
+        return "Please upload a PDF."
+
+    # 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": "input_text", "text": text}, {"type": "input_text", "text": text_query}]}
+    ]
+    return query_openai(messages, temperature, top_p, max_output_tokens)
+
+# Function to clear the chat
+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)
+    clear_button.click(clear_chat, outputs=[image_url, image_query, image_url_output, text_query, text_output, image_upload, 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()