Update app.py

app.py

@@ -1,66 +1,46 @@
 import gradio as gr
-import os
-from groq import Groq
-from googletrans import Translator
-import asyncio
-from groq.client import Client as GroqClient
+from groq.client import Client  # Adjust this based on Groq's official documentation
 
+# Initialize Groq API client
+api_key = "your_groq_api_key"  # Replace with your actual API key
+client = Client(api_key=api_key)
 
-# Function to get recommendations from Groq AI based on user input
-def get_opportunities(user_interests, user_skills, user_location):
-    # Fetch the API key from the environment variable
-    api_key = "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"
-    
-    if not api_key:
-        raise ValueError("API key is missing. Make sure to set the GROQ_API_KEY environment variable.")
-    
-    # Initialize the Groq client with the API key
-    client = Groq(api_key=api_key)
-    
-    # Construct the query
-    query = f"Based on the user's interests in {user_interests}, skills in {user_skills}, and location of {user_location}, find scholarships, internships, online courses, and career advice suitable for them."
-    
-    # Request to Groq API
+# Chatbot logic using Groq API
+def chatbot(message, history):
+    history = history or []  # Initialize history if None
+
+    # Build a prompt using the chat history
+    prompt = "\n".join([f"User: {msg[0]}\nBot: {msg[1]}" for msg in history])
+    prompt += f"\nUser: {message}\nBot:"
+
+    # Query the Groq API
     response = client.chat.completions.create(
-        messages=[{"role": "user", "content": query}],
-        model="llama-3.3-70b-versatile",
+        messages=[{"role": "user", "content": prompt}],
+        model="llama-3.3-70b-versatile",  # Adjust the model name if necessary
     )
-    
-    return response.choices[0].message.content
-
-# Function to translate text into the selected language (async version)
-async def translate_text(text, target_language):
-    translator = Translator()
-    translated = await translator.translate(text, dest=target_language)
-    return translated.text
+    bot_reply = response.choices[0].message.content.strip()  # Extract the reply
 
-# Chatbot interface function
-def chatbot(input_text, interests, skills, location, language):
-    if interests and skills and location:
-        # Fetch recommendations using the Groq API
-        opportunities = get_opportunities(interests, skills, location)
-        
-        # Run the async translate function and get the translated text
-        translated_opportunities = asyncio.run(translate_text(opportunities, language))
-        
-        return translated_opportunities
-    else:
-        return "Please fill all fields: Interests, Skills, and Location."
+    # Append the conversation to history
+    history.append((message, bot_reply))
+    return history, history
 
-# Gradio interface components
+# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# AI-Powered Opportunity Finder for Youth")
-    
+    gr.Markdown("# Gradio Chatbot with Groq API")
+    chatbot_ui = gr.Chatbot(label="Chatbot")  # Chatbot display
     with gr.Row():
-        interests = gr.Textbox(label="Your Interests (e.g., AI, Robotics, Software Engineering):", placeholder="Enter your interests here...")
-        skills = gr.Textbox(label="Your Skills (e.g., Python, Data Science, Web Development):", placeholder="Enter your skills here...")
-        location = gr.Textbox(label="Your Location (e.g., Gujrat, Pakistan):", placeholder="Enter your location here...")
-    
-    language = gr.Dropdown(label="Select your preferred language:", choices=["English", "Spanish", "French", "German", "Italian", "Chinese", "Japanese", "Urdu"], value="English")
-    
-    with gr.Chatbot() as chatbot_output:
-        submit_button = gr.Button("Find Opportunities")
-        submit_button.click(chatbot, inputs=[gr.Textbox(), interests, skills, location, language], outputs=chatbot_output)
+        user_input = gr.Textbox(placeholder="Type your message here...")  # Input box
+        submit_btn = gr.Button("Send")  # Send button
+
+    # Clear history button
+    clear_btn = gr.Button("Clear Chat")
+
+    # State management for chat history
+    state = gr.State()
+
+    # Event bindings
+    submit_btn.click(chatbot, inputs=[user_input, state], outputs=[chatbot_ui, state])
+    clear_btn.click(lambda: ([], None), outputs=[chatbot_ui, state])
 
 # Launch the app
 demo.launch()