Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import transforms
+from torchvision.models import resnet50
+import io
+
+# Streamlit app
+st.title("Stock Trend Predictor: Bullish or Bearish?")
+
+# Load pre-trained ResNet50 model
+@st.cache_resource
+def load_model():
+    model = resnet50(pretrained=True)
+    model.fc = torch.nn.Linear(model.fc.in_features, 2)  # 2 classes: bullish and bearish
+    try:
+        state_dict = torch.load('/Users/kaylahoffman/Desktop/full_model.pt', map_location=torch.device('cpu'))
+        model.load_state_dict(state_dict)
+        st.success("Model loaded successfully!")
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        st.warning("Using untrained model. Predictions may not be accurate.")
+    model.eval()
+    return model
+
+model = load_model()
+
+# Define image transformation
+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]),
+])
+
+uploaded_file = st.file_uploader("Upload a stock graph image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    try:
+        image = Image.open(uploaded_file).convert('RGB')
+        st.image(image, caption="Uploaded Stock Graph", use_column_width=True)
+
+        # Preprocess the image
+        input_tensor = transform(image).unsqueeze(0)
+
+        # Make prediction
+        with torch.no_grad():
+            output = model(input_tensor)
+            probabilities = torch.nn.functional.softmax(output[0], dim=0)
+            predicted_class = torch.argmax(probabilities).item()
+
+        # Display prediction
+        st.header("Prediction")
+        if predicted_class == 0:
+            sentiment = "Bearish"
+            color = "red"
+        else:
+            sentiment = "Bullish"
+            color = "green"
+
+        confidence = probabilities[predicted_class].item() * 100
+        st.subheader(f"{sentiment}: {confidence:.2f}%")
+        st.progress(confidence / 100, text=f"{sentiment} Confidence")
+
+    except Exception as e:
+        st