Update app.py

app.py

@@ -1,31 +1,37 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import fitz  # PyMuPDF
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 client = InferenceClient("opennyaiorg/Aalap-Mistral-7B-v0.1-bf16")
 
+def extract_text_from_pdf(pdf_file):
+    document = fitz.open(pdf_file.name)
+    text = ""
+    for page_num in range(len(document)):
+        page = document.load_page(page_num)
+        text += page.get_text()
+    return text
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def summarize_pdf(pdf_file, max_tokens, temperature, top_p):
+    text = extract_text_from_pdf(pdf_file)
+    response = ""
+    messages = [{"role": "user", "content": f"Summarize the following text: {text}"}]
 
-    messages.append({"role": "user", "content": message})
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+        response += token
+        yield response
 
+def ner_pdf(pdf_file, max_tokens, temperature, top_p):
+    text = extract_text_from_pdf(pdf_file)
     response = ""
+    messages = [{"role": "user", "content": f"Extract named entities from the following text: {text}"}]
 
     for message in client.chat_completion(
         messages,
@@ -35,29 +41,46 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
+        response += token
+        yield response
+
+def qa_pdf(pdf_file, question, max_tokens, temperature, top_p):
+    text = extract_text_from_pdf(pdf_file)
+    response = ""
+    messages = [{"role": "user", "content": f"Answer the question '{question}' based on the following text: {text}"}]
 
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
         response += token
         yield response
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# NLP Tasks on PDF Documents")
 
+    with gr.Tab("Summarization"):
+        pdf_file = gr.File(label="Upload PDF")
+        summarize_button = gr.Button("Summarize")
+        summary_output = gr.Textbox(label="Summary")
+        summarize_button.click(summarize_pdf, inputs=[pdf_file, gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")], outputs=summary_output)
+    
+    with gr.Tab("Named Entity Recognition (NER)"):
+        pdf_file = gr.File(label="Upload PDF")
+        ner_button = gr.Button("Extract Entities")
+        ner_output = gr.JSON(label="Entities")
+        ner_button.click(ner_pdf, inputs=[pdf_file, gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")], outputs=ner_output)
+    
+    with gr.Tab("Question Answering"):
+        pdf_file = gr.File(label="Upload PDF")
+        question_input = gr.Textbox(label="Enter your question")
+        qa_button = gr.Button("Get Answer")
+        qa_output = gr.Textbox(label="Answer")
+        qa_button.click(qa_pdf, inputs=[pdf_file, question_input, gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")], outputs=qa_output)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()