Create app.py

app.py

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+# app.py
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+import numpy as np
+import tensorflow as tf
+from tensorflow.lite.python.interpreter import Interpreter
+import os
+import google.generativeai as genai
+import uvicorn
+from typing import Optional
+from pydantic import BaseModel
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load the TensorFlow Lite model
+interpreter = Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Define categories
+data_cat = ['disposable cups', 'paper', 'plastic bottle']
+img_height, img_width = 224, 224
+
+# Configure Gemini API
+GEMINI_API_KEY = os.getenv('GEMINI_API_KEY', 'AIzaSyBx0A7BA-nKVZOiVn39JXzdGKgeGQqwAFg')
+genai.configure(api_key=GEMINI_API_KEY)
+
+# Initialize Gemini model
+gemini_model = genai.GenerativeModel('gemini-pro')
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        
+        # Preprocess the image
+        img = tf.image.decode_image(contents, channels=3)
+        img = tf.image.resize(img, [img_height, img_width])
+        img_bat = np.expand_dims(img, 0).astype(np.float32)
+
+        # Set input tensor
+        interpreter.set_tensor(input_details[0]['index'], img_bat)
+
+        # Run inference
+        interpreter.invoke()
+
+        # Get the result
+        output_data = interpreter.get_tensor(output_details[0]['index'])
+        predicted_class = data_cat[np.argmax(output_data)]
+        confidence = float(np.max(output_data) * 100)
+
+        # Generate sustainability insights with Gemini API
+        prompt = f"""
+        You are a sustainability-focused AI. Analyze the {predicted_class} (solid dry waste) 
+        and generate the top three innovative, eco-friendly recommendations for repurposing it. 
+        Each recommendation should:
+        - Provide a title
+        - Be practical and easy to implement
+        - Be environmentally beneficial
+        - Include a one or two-sentence explanation
+        Format each recommendation with a clear title followed by the explanation on a new line.
+        """
+        
+        try:
+            response = gemini_model.generate_content(prompt)
+            insights = response.text.strip()
+        except Exception as e:
+            insights = f"Error generating insights: {str(e)}"
+
+        return {
+            "class": predicted_class,
+            "confidence": confidence,
+            "insights": insights
+        }
+    
+    except Exception as e:
+        return {"error": str(e)}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)