Update app.py

app.py

@@ -5,53 +5,166 @@ from config import OPENAI_API_KEY
 import os
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
-def extract_text_from_pdf(pdf_file):
-    text = ""
-    with open(pdf_file.name, "rb") as file:
-        reader = PyPDF2.PdfReader(file)
-        for page in reader.pages:
-            text += page.extract_text() + "\n"
-    return text
-
-def answer_question(pdf_file, question):
-    # Extract text from the PDF
-    text = extract_text_from_pdf(pdf_file)
-    
-    # Define the assistant's behavior
-    assistant_prompt = f"""
-    You are a helpful assistant that answers questions based on the content of the PDF provided.
-    Here is the content of the PDF:
-    {text}
-
-    User question: {question}
-    """
-
-    # Call OpenAI API to get the answer using GPT-4 Turbo
-    response = openai.ChatCompletion.create(
-        model="gpt-4-turbo",
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": assistant_prompt}
+import gradio as gr
+import PyPDF2
+import openai
+from config import OPENAI_API_KEY
+import os
+
+openai.api_key = OPENAI_API_KEY
+
+class PDFChat:
+    def __init__(self):
+        self.pdf_text = ""
+    
+    def extract_text_from_pdf(self, pdf_file):
+        """Extract text from PDF file and store it"""
+        if not pdf_file:
+            return "Please upload a PDF file first."
+        
+        try:
+            self.pdf_text = ""  # Clear previous content
+            with open(pdf_file.name, "rb") as file:
+                reader = PyPDF2.PdfReader(file)
+                for page in reader.pages:
+                    self.pdf_text += page.extract_text() + "\n"
+            return "PDF loaded successfully! You can now ask questions."
+        except Exception as e:
+            return f"Error loading PDF: {str(e)}"
+
+    def answer_question(self, question, chat_history):
+        """Generate answer based on PDF content and conversation history"""
+        if not self.pdf_text:
+            return [[question, "Please upload and load a PDF file first."]]
+
+        if not question:
+            return chat_history
+
+        # Construct the conversation context
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant that answers questions based on the PDF content."},
+            {"role": "system", "content": f"PDF Content: {self.pdf_text}"}
         ]
+        
+        # Add conversation history
+        for human, assistant in chat_history:
+            messages.append({"role": "user", "content": human})
+            messages.append({"role": "assistant", "content": assistant})
+        
+        # Add current question
+        messages.append({"role": "user", "content": question})
+        
+        try:
+            response = openai.ChatCompletion.create(
+                model="gpt-4-turbo",
+                messages=messages
+            )
+            answer = response.choices[0].message['content']
+            
+            # Update chat history with new question and answer
+            chat_history.append((question, answer))
+            return chat_history
+        except Exception as e:
+            error_message = f"Error generating response: {str(e)}"
+            chat_history.append((question, error_message))
+            return chat_history
+
+    def clear_history(self):
+        """Clear conversation history"""
+        return []
+
+css = """
+.container {
+    max-width: 800px;
+    margin: auto;
+}
+.chat-window {
+    height: 600px;
+    overflow-y: auto;
+}
+"""
+
+# Create PDF Chat instance
+pdf_chat = PDFChat()
+
+# Create the Gradio interface
+with gr.Blocks(css=css, theme='Taithrah/Minimal') as demo:
+    gr.Markdown("# Renesas PDF Chatbot")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            pdf_input = gr.File(
+                label="Upload PDF",
+                file_types=[".pdf"]
+            )
+            load_button = gr.Button("Load PDF")
+            status_text = gr.Textbox(
+                label="Status",
+                interactive=False
+            )
+    
+    with gr.Row():
+        chatbot = gr.Chatbot(
+            [],
+            elem_id="chatbot",
+            label="Chat History",
+            height=400
+        )
+    
+    with gr.Row():
+        question_input = gr.Textbox(
+            label="Ask a question",
+            placeholder="What would you like to know about the PDF?",
+            scale=4
+        )
+        submit_button = gr.Button("Send", scale=1)
+        clear_button = gr.Button("Clear History", scale=1)
+    
+    # Example queries
+    gr.Examples(
+        examples=[
+            ["renesas-ra6m1-group-datasheet.pdf", "Which Renesas products are mentioned in this PDF?"],
+            ["renesas-ra6m1-group-datasheet.pdf", "What are the key features of the microcontroller?"],
+            ["renesas-ra6m1-group-datasheet.pdf", "Explain the power consumption specifications."]
+        ],
+        inputs=[pdf_input, question_input],
+        label="Example Queries"
+    )
+    
+    # Event handlers
+    load_button.click(
+        pdf_chat.extract_text_from_pdf,
+        inputs=[pdf_input],
+        outputs=[status_text]
     )
     
-    answer = response.choices[0].message['content']
-    return answer
-
-# Create Gradio interface using the updated input/output classes
-iface = gr.Interface(
-    fn=answer_question,
-    inputs=[
-        gr.File(label="Upload PDF"),
-        gr.Textbox(label="Ask a question about the PDF", placeholder="What do you want to know?")
-    ],
-    outputs="text",
-    title="PDF Q&A with OpenAI Assistant",
-    description="Upload a PDF document and ask questions about its content. The assistant will provide answers based on the PDF.",
-    examples=[
-        ["renesas-ra6m1-group-datasheet.pdf", "Which Renesas products are mentioned in this PDF?"]
-    ]
-)
-
-# Launch the interface
-iface.launch()
+    # Function to clear input after sending
+    def clear_input():
+        return ""
+    
+    question_input.submit(
+        pdf_chat.answer_question,
+        inputs=[question_input, chatbot],
+        outputs=[chatbot]
+    ).then(
+        clear_input,
+        outputs=[question_input]
+    )
+    
+    submit_button.click(
+        pdf_chat.answer_question,
+        inputs=[question_input, chatbot],
+        outputs=[chatbot]
+    ).then(
+        clear_input,
+        outputs=[question_input]
+    )
+    
+    clear_button.click(
+        pdf_chat.clear_history,
+        outputs=[chatbot]
+    )
+
+# Launch the interface with sharing enabled
+if __name__ == "__main__":
+    demo.launch(debug=True)