Update app.py

app.py

@@ -1,30 +1,83 @@
 import gradio as gr
-import joblib
 import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+import requests
+import os
 
-# Load the trained model
-model = joblib.load("model.pkl")
-
-# Define prediction function
-def predict_efficiency(sulfur, gcv):
-    try:
-        input_data = np.array([[sulfur, gcv]])
-        prediction = model.predict(input_data)
-        return f"Predicted Combustion Efficiency: {prediction[0]:.2f}%"
-    except Exception as e:
-        return f"Error: {e}"
-
-# Gradio UI
-demo = gr.Interface(
-    fn=predict_efficiency,
-    inputs=[
-        gr.Number(label="Total Sulfur (%)"),
-        gr.Number(label="GCV (kcal/kg)")
-    ],
-    outputs="text",
-    title="Smart FuelTech: Combustion Efficiency Predictor",
-    description="Enter sulfur and GCV to predict combustion efficiency of coal."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+# === Load API Key from Environment Variable ===
+GROQ_API_KEY = os.environ.get("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", "")
+
+# === Train Model on Sample Data ===
+X_train = np.array([
+    [35, 10, 1, 6500],
+    [40, 20, 2, 3000],
+    [20, 15, 2.5, 4000],
+    [30, 12, 1.2, 5500],
+    [25, 18, 3.0, 4500]
+])
+y_train = np.array([78, 50, 45, 70, 55])
+
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# === Prediction Function ===
+def predict_efficiency(vm, ash, sulfur, gcv):
+    if vm == 0 and ash == 0 and sulfur == 0 and gcv == 0:
+        return 0.0, ""
+    if ash > 50 or gcv < 1000:
+        return 5.0, "Ash is too high or GCV is too low."
+
+    prediction = model.predict([[vm, ash, sulfur, gcv]])[0]
+    prediction = max(0.0, min(100.0, round(prediction, 2)))
+
+    prompt = f"""The following coal sample has:
+- Volatile Matter: {vm}%
+- Ash: {ash}%
+- Total Sulfur: {sulfur}%
+- GCV: {gcv} kcal/kg
+
+The predicted combustion efficiency is {prediction}%.
+
+Give 1 or 2 short suggestions to improve this combustion efficiency."""
+
+    suggestion = ""
+    if GROQ_API_KEY:
+        headers = {
+            "Authorization": f"Bearer {GROQ_API_KEY}",
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "model": "llama3-8b-8192",
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": 0.7
+        }
+        try:
+            response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=payload)
+            suggestion = response.json()["choices"][0]["message"]["content"]
+        except Exception as e:
+            suggestion = f"Error: {str(e)}"
+    else:
+        suggestion = "GROQ_API_KEY not set."
+
+    return prediction, suggestion
+
+# === Gradio UI ===
+with gr.Blocks() as demo:
+    gr.Markdown("## 🔥 Smart FuelTech: AI-Powered Combustion Efficiency Monitor")
+
+    with gr.Row():
+        vm = gr.Number(label="Volatile Matter (%)", value=35)
+        ash = gr.Number(label="Ash Content (%)", value=10)
+        sulfur = gr.Number(label="Total Sulfur (%)", value=1)
+        gcv = gr.Number(label="GCV (kcal/kg)", value=6500)
+
+    with gr.Row():
+        efficiency_output = gr.Number(label="Predicted Efficiency (%)")
+        suggestion_output = gr.Textbox(label="AI Suggestion")
+
+    submit = gr.Button("Predict")
+    submit.click(fn=predict_efficiency,
+                 inputs=[vm, ash, sulfur, gcv],
+                 outputs=[efficiency_output, suggestion_output])
+
+demo.launch()