Create app.py

app.py

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+import gradio as gr
+import PyPDF2
+import openai
+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}
+        ]
+    )
+    
+    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()