Update app.py

app.py

@@ -1,64 +1,66 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from PyPDF2 import PdfReader
+import os
+import openai
 
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
+# Set OpenAI key
+openai.api_key = os.getenv("OPENAI_API_KEY")
 
+pdf_text = ""
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def extract_text_from_pdf(pdf_file):
+    reader = PdfReader(pdf_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text() or ""
+    return text
 
-    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 process_pdf(pdf):
+    global pdf_text
+    pdf_text = extract_text_from_pdf(pdf)
+    return "PDF loaded! Ask anything about it."
 
-    messages.append({"role": "user", "content": message})
+def chat_with_pdf(question):
+    if not pdf_text:
+        return "Please upload and process a PDF first."
+    
+    prompt = f"""You are a helpful assistant. The user uploaded a PDF document. Here's its content:
 
-    response = ""
+--- BEGIN DOCUMENT ---
+{pdf_text}
+--- END DOCUMENT ---
 
-    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
+Now, answer the following question based on the document:
+Q: {question}
+A:"""
 
-        response += token
-        yield response
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",  # or "gpt-4"
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant that answers questions about uploaded PDFs."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=500,
+        temperature=0.3,
+    )
 
+    return response.choices[0].message["content"]
 
-"""
-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("## 🤖 Chat with your PDF (No Chunking, No Embeddings)")
 
+    with gr.Row():
+        pdf_file = gr.File(label="Upload your PDF", file_types=[".pdf"])
+        load_button = gr.Button("Load PDF")
 
-if __name__ == "__main__":
-    demo.launch()
+    status = gr.Textbox(label="Status")
+
+    with gr.Row():
+        question = gr.Textbox(label="Your Question")
+        answer = gr.Textbox(label="Answer", lines=10)
+        ask_button = gr.Button("Ask")
+
+    load_button.click(process_pdf, inputs=pdf_file, outputs=status)
+    ask_button.click(chat_with_pdf, inputs=question, outputs=answer)
+
+demo.launch()