Create app.py

app.py

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+import gradio as gr
+import openai
+import base64
+from PIL import Image
+import io
+import fitz  # PyMuPDF for PDF handling
+
+# Function to extract text from PDF files
+def extract_text_from_pdf(pdf_file):
+    try:
+        text = ""
+        pdf_document = fitz.open(pdf_file)
+        for page_num in range(len(pdf_document)):
+            page = pdf_document[page_num]
+            text += page.get_text()
+        pdf_document.close()
+        return text
+    except Exception as e:
+        return f"Error extracting text from PDF: {str(e)}"
+
+# Function to generate MCQ quiz from PDF content
+def generate_mcq_quiz(pdf_content, num_questions, openai_api_key, model_choice):
+    if not openai_api_key:
+        return "Error: No API key provided."
+    
+    openai.api_key = openai_api_key
+    limited_content = pdf_content[:8000] if len(pdf_content) > 8000 else pdf_content
+    
+    prompt = f"""Based on the following document content, generate {num_questions} multiple-choice quiz questions.
+For each question:
+1. Create a clear question based on key concepts in the document
+2. Provide 4 possible answers (A, B, C, D)
+3. Indicate the correct answer
+4. Briefly explain why the answer is correct
+Format the output clearly with each question numbered and separated.
+Document content:
+{limited_content}
+"""
+    
+    try:
+        response = openai.ChatCompletion.create(
+            model=model_choice,
+            messages=[{"role": "user", "content": prompt}]
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error generating quiz: {str(e)}"
+
+# Function to handle image inputs
+def generate_image_response(input_text, image, openai_api_key, model_choice):
+    if not openai_api_key:
+        return "Error: No API key provided."
+    
+    openai.api_key = openai_api_key
+    
+    # Convert image to base64
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    base64_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    
+    try:
+        response = openai.ChatCompletion.create(
+            model=model_choice,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": input_text},
+                        {"type": "image_url", 
+                         "image_url": {"url": f"data:image/png;base64,{base64_str}"}
+                        }
+                    ]
+                }
+            ],
+            max_tokens=2000
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error processing image: {str(e)}"
+
+# Main chatbot function
+def chatbot(input_text, image, pdf_file, openai_api_key, model_choice, pdf_content, num_quiz_questions, pdf_quiz_mode, history):
+    if history is None:
+        history = []
+    
+    new_pdf_content = pdf_content
+    if pdf_file is not None:
+        new_pdf_content = extract_text_from_pdf(pdf_file)
+    
+    if pdf_quiz_mode:
+        if new_pdf_content:
+            quiz_response = generate_mcq_quiz(new_pdf_content, int(num_quiz_questions), openai_api_key, model_choice)
+            history.append((f"👤: [PDF Quiz - {num_quiz_questions} questions]", f"🤖: {quiz_response}"))
+        else:
+            history.append(("👤: [PDF Quiz]", "🤖: Please upload a PDF file to generate questions."))
+    else:
+        if image is not None:
+            response = generate_image_response(input_text, image, openai_api_key, model_choice)
+            if input_text.strip():
+                history.append((f"👤: {input_text}", f"🤖: {response}"))
+            else:
+                history.append((f"👤: [Image]", f"🤖: {response}"))
+    
+    return "", None, None, new_pdf_content, history
+
+def clear_history():
+    return "", None, None, "", []
+
+def update_input_type(choice):
+    if choice == "Image":
+        return (
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(value=False)
+        )
+    elif choice == "PDF(QUIZ)":
+        return (
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(value=True)
+        )
+
+# Custom CSS styling
+custom_css = """
+    .gradio-container {
+        font-family: 'Arial', sans-serif;
+        background-color: #f0f4f8;
+    }
+    .gradio-header {
+        background: linear-gradient(135deg, #4a00e0 0%, #8e2de2 100%);
+        color: white;
+        padding: 20px;
+        border-radius: 8px;
+        text-align: center;
+    }
+    .gradio-chatbot {
+        background-color: white;
+        border-radius: 10px;
+        padding: 20px;
+        box-shadow: 0 6px 18px rgba(0, 0, 0, 0.1);
+    }
+    #submit-btn {
+        background: linear-gradient(135deg, #4a00e0 0%, #8e2de2 100%);
+        color: white;
+        border-radius: 8px;
+    }
+    #clear-history {
+        background: linear-gradient(135deg, #e53e3e 0%, #f56565 100%);
+        color: white;
+        border-radius: 8px;
+    }
+"""
+
+def create_interface():
+    with gr.Blocks(css=custom_css) as demo:
+        gr.Markdown("""
+            <div class="gradio-header">
+                <h1>Multimodal Chatbot (Image + PDF Quiz)</h1>
+                <h3>Analyze images or generate quizzes from PDFs</h3>
+            </div>
+        """)
+        
+        with gr.Accordion("Instructions", open=False):
+            gr.Markdown("""
+                - **Image Chat**: Upload an image and ask questions about it
+                - **PDF Quiz**: Upload a PDF and generate multiple-choice questions
+                - Always provide your OpenAI API key
+                - Choose appropriate model (o1 for images, o3-mini for text)
+            """)
+        
+        pdf_content = gr.State("")
+        
+        with gr.Row():
+            openai_api_key = gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-...")
+        
+        with gr.Row():
+            input_type = gr.Radio(["Image", "PDF(QUIZ)"], label="Input Type", value="Image")
+        
+        with gr.Row():
+            input_text = gr.Textbox(label="Question (for images)", visible=True)
+            image_input = gr.Image(label="Upload Image", type="pil", visible=True)
+            pdf_input = gr.File(label="Upload PDF", visible=False)
+            quiz_slider = gr.Slider(1, 20, value=5, step=1, label="Number of Questions", visible=False)
+            quiz_mode = gr.Checkbox(visible=False)
+        
+        with gr.Row():
+            model_choice = gr.Dropdown(["o1", "o3-mini"], label="Model", value="o1")
+            submit_btn = gr.Button("Submit", elem_id="submit-btn")
+            clear_btn = gr.Button("Clear History", elem_id="clear-history")
+        
+        chat_history = gr.Chatbot()
+        
+        input_type.change(
+            update_input_type,
+            inputs=[input_type],
+            outputs=[input_text, image_input, pdf_input, quiz_slider, quiz_mode]
+        )
+        
+        submit_btn.click(
+            chatbot,
+            inputs=[input_text, image_input, pdf_input, openai_api_key, model_choice, pdf_content, quiz_slider, quiz_mode, chat_history],
+            outputs=[input_text, image_input, pdf_input, pdf_content, chat_history]
+        )
+        
+        clear_btn.click(
+            clear_history,
+            outputs=[input_text, image_input, pdf_input, pdf_content, chat_history]
+        )
+
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()