app.py

import os
import json
import pandas as pd
from docx import Document
from PyPDF2 import PdfReader
from huggingface_hub import InferenceClient
import gradio as gr

# Retrieve Hugging Face API key from environment variable (secret)
API_KEY = os.getenv("APIHUGGING")
if not API_KEY:
    raise ValueError("Hugging Face API key not found. Please set the 'APIHUGGING' secret.")

# Initialize Hugging Face Inference Client
client = InferenceClient(api_key=API_KEY)

# Function to extract text from various file types
def extract_file_content(file_path):
    _, file_extension = os.path.splitext(file_path.name)
    if file_extension.lower() in [".txt"]:
        return file_path.read().decode("utf-8")
    elif file_extension.lower() in [".csv"]:
        df = pd.read_csv(file_path)
        return df.to_string(index=False)
    elif file_extension.lower() in [".json"]:
        data = json.load(file_path)
        return json.dumps(data, indent=4)
    elif file_extension.lower() in [".pdf"]:
        reader = PdfReader(file_path)
        text = ""
        for page in reader.pages:
            text += page.extract_text()
        return text
    elif file_extension.lower() in [".docx"]:
        doc = Document(file_path)
        return "\n".join([para.text for para in doc.paragraphs])
    else:
        return "Unsupported file type."

# Function to interact with the Hugging Face model
def get_bot_response(file, prompt):
    try:
        # Extract content from the uploaded file
        file_content = extract_file_content(file)

        # Prepare conversation history
        messages = [
            {"role": "user", "content": f"{prompt}\n\nFile Content:\n{file_content}"}
        ]

        # Call Hugging Face API
        bot_response = client.chat_completions.create(
            model="Qwen/Qwen2.5-Coder-32B-Instruct",
            messages=messages,
            max_tokens=500
        )

        # Collect and return the bot's response
        return bot_response.choices[0].message.content
    except Exception as e:
        return f"Error: {str(e)}"

# Gradio Interface
with gr.Blocks() as app:
    gr.Markdown("# 📁 AI File Chat with Hugging Face 🚀")
    gr.Markdown("Upload any file and ask the AI a question based on the file's content!")

    with gr.Row():
        file_input = gr.File(label="Upload File")
        prompt_input = gr.Textbox(label="Enter your question", placeholder="Ask something about the uploaded file...")

    output = gr.Textbox(label="AI Response")

    submit_button = gr.Button("Submit")
    submit_button.click(get_bot_response, inputs=[file_input, prompt_input], outputs=output)

# Launch the Gradio app
if __name__ == "__main__":
    app.launch()