Update app.py

app.py

@@ -1,69 +1,53 @@
+import requests
 import gradio as gr
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras.models import load_model
-from tensorflow.keras.models import load_model
-from tensorflow.keras.losses import MeanSquaredError
+from datetime import datetime
 
-# Load dataset
-df = pd.read_csv("TEMP_ANNUAL_SEASONAL_MEAN.csv")
+# OpenWeather API details
+BASE_URL = "http://api.openweathermap.org/data/2.5/forecast?"
+API_KEY = "2682f6801720ebeb43c93ad55c826c5c"
+DAYS = 5  # Free API allows only 5 days
 
-# Convert 'YEAR' to datetime and set as index
-df.columns = df.columns.str.upper()
-df['YEAR'] = pd.to_datetime(df['YEAR'], format='%Y')
-df.set_index('YEAR', inplace=True)
+def get_weather(city):
+    url = f"{BASE_URL}q={city}&appid={API_KEY}&units=metric"
+    response = requests.get(url).json()
 
-# Select relevant temperature column
-temp_col = 'ANNUAL'
-df[temp_col] = pd.to_numeric(df[temp_col], errors='coerce')  # Handle errors
-data = df[[temp_col]].dropna()
+    # Error handling
+    if response.get("cod") != "200":
+        return "❌ **Invalid city name! Please enter a correct city.**"
 
-# Normalize the data
-scaler = MinMaxScaler()
-data_scaled = scaler.fit_transform(data)
-
-from tensorflow.keras.models import load_model
-from tensorflow.keras.losses import MeanSquaredError
-
-# Load model with custom_objects
-model = load_model("climate_model.h5", custom_objects={"mse": MeanSquaredError()})
-
-
-# Prediction function
-def predict_temperature(year):
-    year = int(year)
+    weather_info = {}
     
-    # Generate input for LSTM
-    sequence_length = 10
-    last_seq = data_scaled[-sequence_length:].reshape(1, sequence_length, 1)
-
-    # Predict
-    prediction_scaled = model.predict(last_seq)
-    predicted_temp = scaler.inverse_transform(prediction_scaled)[0][0]
+    # Extracting one data point per day
+    for entry in response["list"]:
+        date = entry["dt_txt"].split(" ")[0]  # Extract only the date part
+        if date not in weather_info:  # Store only one entry per day
+            weather_info[date] = {
+                "Temperature": f"{entry['main']['temp']}°C",
+                "Humidity": f"{entry['main']['humidity']}%",
+                "Pressure": f"{entry['main']['pressure']} hPa",
+                "Wind Speed": f"{entry['wind']['speed']} m/s",
+                "Rain": f"{entry.get('rain', {}).get('3h', 0)} mm",
+                "Cloud Cover": f"{entry['clouds']['all']}%"  # Extracting cloud data
+            }
+    
+    # Format output as a Markdown table
+    markdown_output = "**📅 5-Day Weather Forecast**\n\n"
+    markdown_output += "| Date       | Temperature | Humidity | Pressure | Wind Speed | Rain | Cloud Cover |\n"
+    markdown_output += "|------------|------------|----------|----------|------------|------|-------------|\n"
 
-    # Generate graph
-    fig, ax = plt.subplots(figsize=(10, 5))
-    ax.plot(df.index[-20:], df[temp_col][-20:], label="Actual", color='blue')
-    ax.axvline(pd.Timestamp(year, 1, 1), color='red', linestyle='dashed', label="Predicted Year")
-    ax.scatter(pd.Timestamp(year, 1, 1), predicted_temp, color='red', marker='o', label=f"Predicted: {predicted_temp:.2f}°C")
-    ax.set_xlabel("Year")
-    ax.set_ylabel("Temperature (°C)")
-    ax.set_title("Actual vs Predicted Annual Mean Temperature in India")
-    ax.legend()
+    for date, details in weather_info.items():
+        markdown_output += f"| {date} | {details['Temperature']} | {details['Humidity']} | {details['Pressure']} | {details['Wind Speed']} | {details['Rain']} | {details['Cloud Cover']} |\n"
 
-    return predicted_temp, fig  # Return numeric output and graph
+    return markdown_output
 
 # Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("## Climate Change Temperature Prediction")
-    year_input = gr.Number(label="Enter Year ", value=0)
-    predict_button = gr.Button("Predict")
-    output_temp = gr.Number(label="Predicted Temperature (°C)")
-    output_plot = gr.Plot()
-
-    predict_button.click(predict_temperature, inputs=[year_input], outputs=[output_temp, output_plot])
-
-demo.launch()
+iface = gr.Interface(
+    fn=get_weather,
+    inputs=gr.Textbox(label="Enter City Name"),
+    outputs=gr.Markdown(),
+    title="🌍 5-Day Weather Forecast",
+    description="Enter a city name to get temperature, humidity, pressure, wind speed, rain, and cloud cover data for the next **5 days**.",
+)
+
+if __name__ == "__main__":
+    iface.launch()