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Create app.py
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app.py
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import gradio as gr
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import spaces
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import PyPDF2
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import docx
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import io
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import os
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from typing import Optional
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from huggingface_hub import InferenceClient
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from prompts import SYSTEM_PROMPT, PROMPTS
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def extract_text_from_file(file) -> str:
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"""Extract text from uploaded files"""
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if file is None:
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return ""
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file_path = file.name
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text = ""
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try:
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if file_path.endswith('.pdf'):
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with open(file_path, 'rb') as f:
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reader = PyPDF2.PdfReader(f)
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for page in reader.pages:
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text += page.extract_text() + "\n"
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elif file_path.endswith('.docx'):
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doc = docx.Document(file_path)
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for paragraph in doc.paragraphs:
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text += paragraph.text + "\n"
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elif file_path.endswith('.txt'):
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with open(file_path, 'r', encoding='utf-8') as f:
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text = f.read()
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except Exception as e:
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return f"Error reading file: {str(e)}"
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return text
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@spaces.GPU
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def process_document(document, operation_type, text_input):
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"""Main processing function using Cerebras Llama through HuggingFace"""
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# Extract text from file or use text input
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if document is not None:
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text = extract_text_from_file(document)
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else:
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text = text_input
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if not text.strip():
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return "Please provide either a document or text input."
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# Get the appropriate prompt
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prompt = PROMPTS.get(operation_type, "")
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# Create the client with Cerebras provider
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try:
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client = InferenceClient(
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"meta-llama/Llama-3.3-70B-Instruct",
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provider="cerebras",
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token=os.getenv("HF_TOKEN"),
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)
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# Create conversation messages
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"{prompt}\n\nDocument content:\n{text}"}
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]
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# Generate response using chat completion
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response = client.chat_completion(
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messages=messages,
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max_tokens=3000,
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temperature=0.1,
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stream=False
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)
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return response.choices[0].message.content
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except Exception as e:
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return f"Error: {str(e)}\n\nPlease ensure:\n1. HF_TOKEN is set in settings\n2. You have Pro access to use Cerebras inference\n3. The Cerebras/Llama integration is enabled in your account"
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# Create the Gradio interface
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with gr.Blocks(title="Study Assistant", theme=gr.themes.Soft()) as demo:
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gr.Markdown("# π Study Assistant - Document Analysis Tool")
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gr.Markdown("Upload a document or paste text, then select the type of analysis you want to perform.")
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gr.Markdown("*Powered by Meta Llama-3.3-70B via Cerebras on HuggingFace*")
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with gr.Row():
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with gr.Column():
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document = gr.File(
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label="Upload Document",
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file_types=[".pdf", ".docx", ".txt"],
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file_count="single"
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)
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text_input = gr.Textbox(
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label="Or paste text directly",
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lines=5,
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placeholder="Paste your text here if you don't want to upload a file..."
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)
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with gr.Column():
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operation_type = gr.Dropdown(
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choices=["Summary", "Outline", "Analysis", "Study Guide", "Table", "Questions"],
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label="Select Operation",
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value="Summary"
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)
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process_btn = gr.Button("π Process Document", variant="primary", size="lg")
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output = gr.Textbox(
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label="Output",
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lines=20,
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show_copy_button=True
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)
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gr.Markdown("---")
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gr.Markdown("### Tips:")
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gr.Markdown("- Supported formats: PDF, DOCX, TXT")
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gr.Markdown("- Maximum file size: 200MB")
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gr.Markdown("- Text can be pasted directly if you don't have a file")
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gr.Markdown("- Requires HuggingFace Pro account with Cerebras access")
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process_btn.click(
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fn=process_document,
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inputs=[document, operation_type, text_input],
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outputs=output,
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show_progress=True
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)
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if __name__ == "__main__":
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demo.launch()
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