Create app.py

app.py

@@ -0,0 +1,124 @@
+import gradio as gr
+from langchain_core.prompts import PromptTemplate
+from langchain.chains.summarize import load_summarize_chain
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_openai import ChatOpenAI
+from langchain_community.callbacks import get_openai_callback
+import os
+from dotenv import load_dotenv
+
+os.makedirs("data", exist_ok=True)
+
+load_dotenv()
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+def summarize_pdf(pdf_file, custom_prompt="", openai_api_key=None):
+    """
+    Summarizes the content of a PDF file using a custom prompt.
+
+    Args:
+        pdf_file (UploadedFile): The uploaded PDF file.
+        custom_prompt (str): The prompt for summarization.
+        openai_api_key (str, optional): User-provided OpenAI API key.
+
+    Returns:
+        tuple: Summary in markdown format and the cost in USD.
+    """
+    pdf_path = os.path.join("data", "tmp.pdf")
+    with open(pdf_path, "wb") as f:
+        f.write(pdf_file)
+
+    api_key = openai_api_key if openai_api_key else OPENAI_API_KEY
+    
+    if not api_key:
+        return "Error: No OpenAI API key provided.", "N/A"
+
+    with get_openai_callback() as cb:
+        try:
+            model = ChatOpenAI(
+                model="gpt-4o-mini", 
+                temperature=0,
+                openai_api_key=api_key
+            )
+
+            loader = PyPDFLoader(pdf_path)
+            docs = loader.load_and_split()
+            
+            if not custom_prompt.strip():
+                custom_prompt = default_prompt
+
+            prompt_template = (
+                custom_prompt
+                + """
+
+            {text}
+
+            SUMMARY:"""
+            )
+            PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+            chain = load_summarize_chain(
+                model, 
+                chain_type="map_reduce", 
+                map_prompt=PROMPT, 
+                combine_prompt=PROMPT
+            )
+            summary = chain({"input_documents": docs}, return_only_outputs=True)["output_text"]
+            total_cost = cb.total_cost
+
+            return summary, f"${total_cost:.4f}"
+        
+        except Exception as e:
+            return f"An error occurred: {str(e)}", "N/A"
+
+default_prompt = (
+    "Summarize this paper. Return markdown, keep it in a language that scientists understand, "
+    "but the purpose is to highlight the key takeaways, so that we save time for the reader."
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# PDF Summarizer 📝")
+    gr.Markdown("Upload a PDF, customize your summarization prompt, and get a concise summary along with the processing cost.")
+
+    with gr.Row():
+        with gr.Column():
+            if OPENAI_API_KEY is None:
+                api_key_input = gr.Textbox(
+                    label="OpenAI API Key",
+                    type="password",
+                    placeholder="Enter your OpenAI API key."
+                )
+            else:
+                api_key_input = gr.Textbox(
+                    label="OpenAI API Key (Optional)",
+                    type="password",
+                    placeholder="Enter your OpenAI API key if you want to override the global key."
+                )
+            prompt_input = gr.Textbox(
+                label="Custom Prompt",
+                lines=4,
+                value=default_prompt,
+                placeholder="Enter your custom summarization prompt here..."
+            )
+            pdf_input = gr.File(
+                label="Upload PDF",
+                type="binary",
+                file_types=[".pdf"],
+            )
+            summarize_btn = gr.Button("Summarize")
+        
+        with gr.Column():
+            cost_output = gr.Textbox(label="Approximate Cost (USD)", interactive=False)
+            summary_output = gr.Markdown(label="Summary")
+            
+    
+    summarize_btn.click(
+        fn=summarize_pdf,
+        inputs=[pdf_input, prompt_input, api_key_input],
+        outputs=[summary_output, cost_output]
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("Created by [Daniel Herman](https://www.hermandaniel.com)")
+    
+if __name__ == "__main__":
+    demo.launch()