Update appImage.py

appImage.py

@@ -1,73 +1,45 @@
-import gradio as gr
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import RedirectResponse, JSONResponse
 from transformers import AutoProcessor, AutoModelForCausalLM
 from PIL import Image
+import tempfile
 import torch
-from fastapi import FastAPI
-from fastapi.responses import RedirectResponse
 
-# Initialize FastAPI
 app = FastAPI()
 
-# Load models - Using microsoft/git-large-coco
+# Load model
 try:
-    # Load the better model
     processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
-    git_model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
-    print("Successfully loaded microsoft/git-large-coco model")
+    model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
     USE_GIT = True
-except Exception as e:
-    print(f"Failed to load GIT model: {e}. Falling back to smaller model")
+except Exception:
+    from transformers import pipeline
     captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
     USE_GIT = False
 
 def generate_caption(image_path):
-    """Generate caption using the best available model"""
     try:
         if USE_GIT:
             image = Image.open(image_path)
             inputs = processor(images=image, return_tensors="pt")
-            outputs = git_model.generate(**inputs, max_length=50)
+            outputs = model.generate(**inputs, max_length=50)
             return processor.batch_decode(outputs, skip_special_tokens=True)[0]
         else:
             result = captioner(image_path)
             return result[0]['generated_text']
     except Exception as e:
-        print(f"Caption generation error: {e}")
-        return "Could not generate caption"
+        return f"Error generating caption: {str(e)}"
 
-def process_image(file_path: str):
-    """Handle image processing for Gradio interface"""
-    if not file_path:
-        return "Please upload an image first"
-    
-    try:
-        caption = generate_caption(file_path)
-        return f"📷 Image Caption:\n{caption}"
-    except Exception as e:
-        return f"Error processing image: {str(e)}"
-
-# Gradio Interface
-with gr.Blocks(title="Image Captioning Service", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🖼️ Image Captioning Service")
-    gr.Markdown("Upload an image to get automatic captioning")
-    
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(label="Upload Image", type="filepath")
-            analyze_btn = gr.Button("Generate Caption", variant="primary")
-        
-        with gr.Column():
-            output = gr.Textbox(label="Caption Result", lines=5)
-    
-    analyze_btn.click(
-        fn=process_image,
-        inputs=[image_input],
-        outputs=[output]
-    )
+@app.post("/imagecaption/")
+async def caption_from_frontend(file: UploadFile = File(...)):
+    contents = await file.read()
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
+        tmp.write(contents)
+        image_path = tmp.name
 
-# Mount Gradio app to FastAPI
-app = gr.mount_gradio_app(app, demo, path="/")
+    caption = generate_caption(image_path)
+    return JSONResponse({"caption": caption})
 
 @app.get("/")
-def redirect_to_interface():
-    return RedirectResponse(url="/")
+def home():
+    return RedirectResponse(url="/")