Update app.py

app.py

@@ -1,64 +1,93 @@
-import streamlit as st
+import os
+import gradio as gr
+import whisper
+from gtts import gTTS
+import io
 import requests
 from groq import Groq
+import time
 
-# Set up API keys and URLs
-# Use your OpenWeatherMap API key
-openweather_api_key = "aa4db8152e46c2f3fb19fad5d58a0ed8ddssd"
-openweather_api_url = "https://api.openweathermap.org/data/2.5/weather"
+# Ensure GROQ_API_KEY and OpenWeather API key are defined
+GROQ_API_KEY = "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"
+OPENWEATHER_API_KEY = "aa4db8152e46c2f3fb19fad5d58a0ed8"
+OPENWEATHER_URL = "https://api.openweathermap.org/data/2.5/weather"
 
-# Set up Groq API
-groq_api_key = "gsk_loI5Z6fHhtPZo25YmryjWGdyb3FYdfrw1oxGVCfZkwXRE79BAgHCO7c"
-client = Groq(api_key=groq_api_key)
+if not GROQ_API_KEY:
+    raise ValueError("GROQ_API_KEY is not set in environment variables.")
+if not OPENWEATHER_API_KEY:
+    raise ValueError("OPENWEATHER_API_KEY is not set in environment variables.")
 
-# Streamlit UI
-st.title("Real-time Weather App")
+# Initialize the Groq client
+client = Groq(api_key=GROQ_API_KEY)
 
-# City input
-city = st.text_input("Enter city name")
+# Load the Whisper model
+model = whisper.load_model("base")  # Ensure this model supports Urdu; otherwise, choose a suitable model
 
-# Weather data display
-if city:
-    # OpenWeatherMap API request
-    params = {
-        "q": city,
-        "appid": openweather_api_key,
-        "units": "metric"  # For temperature in Celsius; use "imperial" for Fahrenheit
-    }
-    
+def fetch_weather():
     try:
-        response = requests.get(openweather_api_url, params=params)
-        response.raise_for_status()  # Will raise an HTTPError for bad responses
-
+        response = requests.get(
+            OPENWEATHER_URL,
+            params={
+                'q': 'London',  # Change to dynamic city name as needed
+                'appid': OPENWEATHER_API_KEY,
+                'units': 'metric'
+            }
+        )
+        response.raise_for_status()
         weather_data = response.json()
+        temp = weather_data['main']['temp']
+        weather_description = weather_data['weather'][0]['description']
+        return f"Current temperature is {temp}°C with {weather_description}."
+    except Exception as e:
+        return f"Unable to fetch weather data: {e}"
+
+def process_audio(file_path):
+    try:
+        # Load the audio file
+        audio = whisper.load_audio(file_path)
 
-        if weather_data.get("cod") != 200:
-            st.write(f"Error fetching weather data: {weather_data.get('message', 'Unknown error')}")
+        # Transcribe the audio using Whisper (specify language if needed)
+        result = model.transcribe(audio, language="ur")  # Specify 'ur' for Urdu
+        text = result["text"]
+
+        # Check if the text contains any weather-related keywords
+        weather_keywords = ['weather', 'temperature', 'climate', 'forecast']
+        if any(keyword in text.lower() for keyword in weather_keywords):
+            weather_info = fetch_weather()
+            response_message = f"The weather update: {weather_info}"
         else:
-            # Display weather data
-            st.write("Current Weather:")
-            st.write(f"City: {weather_data['name']}, {weather_data['sys']['country']}")
-            st.write(f"Temperature: {weather_data['main']['temp']}°C")
-            st.write(f"Weather: {weather_data['weather'][0]['description'].capitalize()}")
-            st.write(f"Humidity: {weather_data['main']['humidity']}%")
-            st.write(f"Wind Speed: {weather_data['wind']['speed']} m/s")
-    
-    except requests.exceptions.RequestException as e:
-        st.write(f"Error fetching weather data: {e}")
+            # Generate a response in Urdu using Groq
+            chat_completion = client.chat.completions.create(
+                messages=[{"role": "user", "content": text}],
+                model="gemma2-9b-it",  # Ensure this model can handle Urdu
+            )
+            # Access the response using dot notation
+            response_message = chat_completion.choices[0].message.content.strip()
+
+        # Convert the response text to Urdu speech
+        tts = gTTS(response_message, lang='ur')  # Specify language 'ur' for Urdu
+        response_audio_io = io.BytesIO()
+        tts.write_to_fp(response_audio_io)  # Save the audio to the BytesIO object
+        response_audio_io.seek(0)
+
+        # Generate a unique filename
+        response_audio_path = "response_" + str(int(time.time())) + ".mp3"
+        
+        # Save audio to a file
+        with open(response_audio_path, "wb") as audio_file:
+            audio_file.write(response_audio_io.getvalue())
+
+        # Return the response text and the path to the saved audio file
+        return response_message, response_audio_path
+
+    except Exception as e:
+        return f"An error occurred: {e}", None
+
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(type="filepath"),  # Use type="filepath"
+    outputs=[gr.Textbox(label="Response Text (Urdu)"), gr.Audio(label="Response Audio (Urdu)")],
+    live=True  # Set to False if you do not need real-time updates
+)
 
-    # Remove Groq API request and response handling
-    # try:
-    #     chat_completion = client.chat.completions.create(
-    #         messages=[
-    #             {
-    #                 "role": "user",
-    #                 "content": "Explain the importance of fast language models",
-    #             }
-    #         ],
-    #         model="llama3-8b-8192",
-    #     )
-    #     st.write("Groq AI Response:")
-    #     st.write(chat_completion.choices[0].message.content)
-    
-    # except Exception as e:
-    #     st.write(f"Error fetching Groq data: {e}")
+iface.launch()