Rename app.txt to app.py

app.py

@@ -0,0 +1,32 @@
+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse
+from transformers import pipeline
+import gradio as gr
+
+# Load the model pipeline
+pipe = pipeline("image-classification", "dima806/medicinal_plants_image_detection")
+
+# Define the image classification function
+def image_classifier(image):
+    # Perform image classification
+    outputs = pipe(image)
+    results = {}
+    for result in outputs:
+        results[result['label']] = result['score']
+    return results
+
+# Define Gradio Interface
+gr_interface = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="label")
+
+# Define FastAPI app
+app = FastAPI()
+
+# Define route for Gradio interface
+@app.get("/")
+async def gr_interface_route():
+    return HTMLResponse(gr_interface.launch(inline=False, inbrowser=True))
+
+# Expose the FastAPI app using Uvicorn
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

app.txt

@@ -1,44 +0,0 @@
-import streamlit as st
-import torch
-from PIL import Image
-from torchvision import transforms
-from model import ResNet50  # Assuming your model architecture is defined in a separate file called model.py
-
-# Load the model
-model = ResNet50()
-model.load_state_dict(torch.load('best_modelv2.pth', map_location=torch.device('cpu')))
-model.eval()
-
-# Define transform for input images
-data_transforms = 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 predict image label
-def predict_image_label(image):
-    # Preprocess the image
-    image = data_transforms(image).unsqueeze(0)
-
-    # Make prediction
-    with torch.no_grad():
-        output = model(image)
-        _, predicted = torch.max(output, 1)
-    
-    return predicted.item()
-
-# Streamlit app
-st.title("Leaf or Plant Classifier")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Display the uploaded image
-    image = Image.open(uploaded_file)
-    st.image(image, caption='Uploaded Image', use_column_width=True)
-
-    # Classify the image
-    prediction = predict_image_label(image)
-    label = 'Leaf' if prediction == 0 else 'Plant'
-    st.write(f"Prediction: {label}")