Create app.py

app.py

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+from groq import Groq
+import os
+from PIL import Image
+import torch
+from torchvision import transforms
+import gradio as gr
+
+# Replace 'your_api_key_here' with your actual Groq API key
+api_key = "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"
+
+# Initialize the Groq client with the API key
+client = Groq(api_key=api_key)
+
+# Placeholder for flood prediction model (open-source PyTorch model)
+class FloodPredictionModel:
+    def __init__(self):
+        # Example: Load a pre-trained model (you should replace this with a flood prediction model)
+        self.model = torch.hub.load("pytorch/vision:v0.10.0", "resnet18", pretrained=True)
+        self.model.eval()
+
+    def predict(self, image):
+        # Preprocess the image
+        preprocess = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ])
+        input_tensor = preprocess(image).unsqueeze(0)
+
+        # Make prediction
+        with torch.no_grad():
+            outputs = self.model(input_tensor)
+            _, predicted = outputs.max(1)
+        return predicted.item()  # Placeholder: Replace with actual prediction logic
+
+
+# Instantiate the flood prediction model
+flood_model = FloodPredictionModel()
+
+
+# Function to process user input and predict
+def flood_prediction(image, user_query):
+    # Debugging: Check if image is received correctly
+    print("Received Image:", image)
+    
+    # Analyze the image with the flood prediction model
+    try:
+        prediction = flood_model.predict(image)
+        prediction_text = (
+            "Flood risk detected in the area!" if prediction == 1 else "No immediate flood risk detected."
+        )
+    except Exception as e:
+        prediction_text = f"Error in flood prediction: {str(e)}"
+
+    # Debugging: Check prediction result
+    print("Flood Prediction Result:", prediction_text)
+
+    # Use Groq's API for query-based interaction
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": user_query}],
+            model="llama-3.3-70b-versatile",
+        )
+        ai_response = chat_completion.choices[0].message.content
+    except Exception as e:
+        ai_response = f"Error with Groq API: {str(e)}"
+
+    # Debugging: Check AI response
+    print("AI Response:", ai_response)
+
+    return prediction_text, ai_response
+
+
+# Define the Gradio interface
+with gr.Blocks() as flood_app:
+    gr.Markdown("## 🌊 Flood Prediction App")
+    gr.Markdown(
+        """
+        Welcome to the Flood Prediction App! This tool helps you analyze uploaded images
+        to predict potential flood risks in the area. You can also interact with a powerful
+        language model for further insights.
+        
+        ### Instructions:
+        1. Upload an image of the area you want to analyze.
+        2. Optionally, enter a query (e.g., "What are the risks of flooding in coastal areas?").
+        3. Click **Predict Flood Risk** to get the results.
+        """
+    )
+
+    with gr.Row():
+        image_input = gr.Image(label="Upload Image", type="pil")
+        user_query = gr.Textbox(label="Your Query (Optional)", placeholder="Ask about flood risks...")
+    
+    predict_button = gr.Button("Predict Flood Risk")
+    
+    with gr.Row():
+        prediction_output = gr.Textbox(label="Flood Prediction")
+        ai_response_output = gr.Textbox(label="AI Response")
+
+    predict_button.click(
+        flood_prediction, 
+        inputs=[image_input, user_query], 
+        outputs=[prediction_output, ai_response_output]
+    )
+
+    gr.Markdown(
+        """
+        ### Output Description:
+        - **Flood Prediction**: Indicates whether there is a flood risk based on the uploaded image.
+        - **AI Response**: Provides detailed insights or answers based on your query.
+        """
+    )
+
+# Launch the app (for local testing or Google Colab)
+flood_app.launch()