app.bak

import base64
import requests
import os
from mistralai import Mistral
import gradio as gr
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

api_key = os.getenv("MISTRAL_API_KEY")
Mistralclient = Mistral(api_key=api_key)

def encode_image(image_path):
    """Encode the image to base64."""
    try:
        with open(image_path, "rb") as image_file:
            return base64.b64encode(image_file.read()).decode('utf-8')
    except FileNotFoundError:
        print(f"Error: The file {image_path} was not found.")
        return None
    except Exception as e:
        print(f"Error: {e}")
        return None

def feifeichat(image_path):
    try:
        model = "pixtral-large-2411"
        # Encode the input image to base64
        base64_image = encode_image(image_path)
        if not base64_image:
            return "Failed to encode the image."

        # Define the messages for the chat
        messages = [{
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Please provide a detailed description of this photo"
                },
                {
                    "type": "image_url",
                    "image_url": f"data:image/jpeg;base64,{base64_image}" 
                },
            ],
            "stream": False,
        }]
    
        partial_message = ""
        for chunk in Mistralclient.chat.stream(model=model, messages=messages):
            if chunk.data.choices[0].delta.content is not None:
                partial_message += chunk.data.choices[0].delta.content
        return partial_message
    except Exception as e:
        print(f"Error: {e}")
        return "Please upload a valid photo."

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("Florence-2 Image To Flux Prompt")
    with gr.Tab(label="Image To Flux Prompt"):
        with gr.Row():
            with gr.Column():
                input_img = gr.Image(label="Input Picture", height=480, type="filepath")
                submit_btn = gr.Button(value="Submit")
            with gr.Column():
                output_text = gr.Textbox(label="Flux Prompt")

        submit_btn.click(feifeichat, [input_img], [output_text])

# Add FastAPI for Remote Access
app = FastAPI()

class PredictRequest(BaseModel):
    image_base64: str  # Expecting a base64-encoded string

@app.post("/api/predict")
def predict(request: PredictRequest):
    try:
        # Decode the base64 image and save it temporarily
        image_data = base64.b64decode(request.image_base64)
        temp_image_path = "temp_image.jpg"
        with open(temp_image_path, "wb") as temp_image:
            temp_image.write(image_data)

        # Run the prediction function
        response = feifeichat(temp_image_path)
        return {"prediction": response}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during prediction: {e}")

demo.launch(app, share=True)