Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+import torch
+from PIL import Image
+from torchvision import transforms
+from llama_cpp import Llama
+from torchvision.models import vgg16
+
+# Load your gguf model (LLaMA or similar)
+llm = Llama(model_path="path/to/ggml-model-IQ3_M.gguf")
+
+# Load a pre-trained image recognition model (VGG16 in this case)
+vgg_model = vgg16(pretrained=True).eval()
+
+# Image transformation pipeline
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Function to process the image and extract features
+def process_image(image):
+    image_tensor = transform(image).unsqueeze(0)  # Add batch dimension
+    with torch.no_grad():
+        image_features = vgg_model(image_tensor).flatten().tolist()  # Flatten feature map
+    return image_features
+
+# Function to combine the image features with the question and get a chatbot response
+def chatbot(image, question):
+    # Process the image to extract features
+    image_features = process_image(image)
+
+    # Create a prompt combining the question and image features
+    prompt = f"Image features: {image_features}\nQuestion: {question}\nAnswer:"
+
+    # Generate response using the gguf model
+    response = llm(prompt=prompt, max_tokens=128)
+    
+    # Return the chatbot's response
+    return response["choices"][0]["text"].strip()
+
+# Gradio interface
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=[
+        gr.inputs.Image(type="pil"),  # Image input
+        gr.inputs.Textbox(lines=2, placeholder="Ask something about the image...")  # Text input
+    ],
+    outputs="text",  # Text output
+    title="Image to Text Chatbot",
+    description="Upload an image and ask a question to the chatbot. It will try to answer based on the image and your question."
+)
+
+# Launch the Gradio app
+iface.launch()