Update app.py

app.py

@@ -1,124 +1,18 @@
-# Set OpenAI API Key
-openai.api_key = "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"
+import gradio as gr
+from transformers import pipeline
 
-# Load the CLIP model for object identification
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-# Predefined object labels for CLIP
-object_labels = [
-    "cat", "dog", "house", "tree", "car", "mountain", "flower", "bird", "person", "robot",
-    "a digital artwork", "a portrait", "a landscape", "a futuristic cityscape"
-]
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
 
-def analyze_contrast_opencv(image_path):
-    """Analyze the contrast of the uploaded image using OpenCV."""
-    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
-    contrast = img.std()  # Standard deviation of pixel intensities
-    return contrast
-
-def identify_objects_with_clip(image_path):
-    """Identify objects in the image using CLIP."""
-    image = Image.open(image_path).convert("RGB")
-    inputs = clip_processor(text=object_labels, images=image, return_tensors="pt", padding=True)
-    
-    with torch.no_grad():
-        outputs = clip_model(**inputs)
-        logits_per_image = outputs.logits_per_image
-        probs = logits_per_image.softmax(dim=1).numpy().flatten()
-    
-    # Get the label with the highest probability
-    best_match_idx = probs.argmax()
-    best_match_label = object_labels[best_match_idx]
-    return best_match_label
-
-def enhance_contrast(image):
-    """Enhance the contrast of the image."""
-    enhancer = ImageEnhance.Contrast(image)
-    enhanced_image = enhancer.enhance(2.0)  # Increase contrast
-    enhanced_path = "enhanced_image.png"
-    enhanced_image.save(enhanced_path)
-    return enhanced_path
-
-def suggest_improvements_with_chatgpt(object_name):
-    """Generate improvement suggestions for the identified object using ChatGPT."""
-    prompt = f"Suggest ways to improve a digital artwork featuring a {object_name}."
-    response = openai.ChatCompletion.create(
-        model="gpt-4",
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant providing expert art improvement suggestions."},
-            {"role": "user", "content": prompt}
-        ]
-    )
-    return response["choices"][0]["message"]["content"]
-
-def diane_multimodal(message, history):
-    """
-    Process both text and image input:
-    - Respond to text prompts about digital art.
-    - Analyze image contrast, identify objects, and provide feedback.
-    - Enhance contrast or suggest improvements when requested.
-    """
-    response = ""
-    num_images = len(message["files"])
-
-    if num_images > 0:
-        # Handle uploaded images
-        last_image_path = message["files"][-1]
-        contrast = analyze_contrast_opencv(last_image_path)
-        identified_object = identify_objects_with_clip(last_image_path)
-
-        if contrast < 25:  # Adjust threshold as needed
-            response = (
-                f"Hey, great drawing of a {identified_object}! However, it looks like the contrast is too low. "
-                "Would you like me to improve it?"
-            )
-            # Save the analyzed image path to the history
-            history.append({"role": "assistant", "content": (last_image_path,)})
-        else:
-            response = (
-                f"Hey, great drawing of a {identified_object}! Looks like the color contrast is great, be proud of yourself :)"
-            )
-        return {"role": "assistant", "content": response}
-
-    elif message["text"]:
-        # Handle text-based inputs
-        user_text = message["text"].lower()
-        if any(keyword in user_text for keyword in ["improve", "yes", "better"]):
-            # Check if an image was previously uploaded
-            for entry in reversed(history):
-                if isinstance(entry["content"], tuple):  # Check for image in history
-                    last_image_path = entry["content"][0]
-                    enhanced_image_path = enhance_contrast(Image.open(last_image_path).convert("RGB"))
-                    # Return the text message first
-                    history.append({"role": "assistant", "content": "Here's your improved image! Let me know if you'd like further adjustments."})
-                    # Return the image in a separate message
-                    return {"role": "assistant", "content": gr.Image(enhanced_image_path)}
-        elif "suggestions" in user_text:
-            for entry in reversed(history):
-                if isinstance(entry["content"], tuple):  # Check for image in history
-                    last_image_path = entry["content"][0]
-                    identified_object = identify_objects_with_clip(last_image_path)
-                    improvement_suggestions = suggest_improvements_with_chatgpt(identified_object)
-                    return {"role": "assistant", "content": improvement_suggestions}
-        else:
-            response = "Feel free to upload an image or ask for art tips!"
-    
-    return {"role": "assistant", "content": response}
-
-
-# Define the multimodal chatbot interface
-demo = gr.ChatInterface(
-    fn=diane_multimodal,
-    type="messages",
-    examples=[
-        {"text": "Teach me about digital art tools", "files": []},
-        {"text": "What is the best way to do shading digitally?", "files": []},
-    ],
-    multimodal=True,
-    textbox=gr.MultimodalTextbox(file_count="multiple", file_types=["image"]),
-    title="Your Digital Art Nice Expert (DIANE)",
-    description="Use this chatbot to improve your digital art skills or analyze and enhance image contrast.",
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
 )
 
-demo.launch()
+if __name__ == "__main__":
+    gradio_app.launch()