Update app.py

app.py

@@ -1,47 +1,49 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
-import torch
-
-
-title = "🤖AI ChatBot"
-description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
-examples = [["How are you?"]]
-
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
-
-
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(
-        input + tokenizer.eos_token, return_tensors="pt"
-    )
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response
-    history = model.generate(
-        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
-    ).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    # print('decoded_response-->>'+str(response))
-    response = [
-        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
-    ]  # convert to tuples of list
-    # print('response-->>'+str(response))
-    return response, history
-
-
-gr.Interface(
-    fn=predict,
-    title=title,
-    description=description,
-    examples=examples,
-    inputs=["text", "state"],
-    outputs=["chatbot", "state"],
-    theme="finlaymacklon/boxy_violet",
-).launch()
+import requests
+
+API_KEY = "91b23cab82ee530b2052c8757e343b0d"
+BASE_URL = "http://api.openweathermap.org/data/2.5/weather"
+
+def get_weather(city_name):
+    """Fetch weather information for a city using OpenWeatherMap API."""
+    params = {
+        'q': city_name,
+        'appid': API_KEY,
+        'units': 'metric'  # This will return temperature in Celsius
+    }
+    response = requests.get(BASE_URL, params=params)
+    data = response.json()
+    
+    if data['cod'] == 200:
+        return {
+            'City': city_name,
+            'Temperature': data['main']['temp'],
+            'Weather': data['weather'][0]['description'],
+            'Humidity': data['main']['humidity'],
+            'Wind Speed': data['wind']['speed']
+        }
+    else:
+        return {
+            'City': city_name,
+            'Error': data['message']
+        }
+
+def compare_weather(city1, city2):
+    """Compare weather of two cities."""
+    weather1 = get_weather(city1)
+    weather2 = get_weather(city2)
+    
+    return weather1, weather2
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=compare_weather,
+    inputs=[gr.inputs.Textbox(placeholder="Enter first city..."), 
+            gr.inputs.Textbox(placeholder="Enter second city...")],
+    outputs=[gr.outputs.JSON(label="City 1 Weather"), 
+             gr.outputs.JSON(label="City 2 Weather")],
+    live=True
+)
+
+if __name__ == "__main__":
+    iface.launch()