saherPervaiz commited on
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9688ff7
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1 Parent(s): 513ab50

Update app.py

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  1. app.py +58 -58
app.py CHANGED
@@ -1,64 +1,64 @@
1
  import gradio as gr
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- from huggingface_hub import InferenceClient
 
 
 
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- """
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- For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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- """
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- client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- def respond(
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- message,
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- history: list[tuple[str, str]],
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- system_message,
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- max_tokens,
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- temperature,
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- top_p,
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- ):
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- messages = [{"role": "system", "content": system_message}]
 
 
 
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- for val in history:
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- if val[0]:
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- messages.append({"role": "user", "content": val[0]})
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- if val[1]:
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- messages.append({"role": "assistant", "content": val[1]})
 
 
 
 
 
 
 
 
 
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- messages.append({"role": "user", "content": message})
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-
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- response = ""
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-
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- for message in client.chat_completion(
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- messages,
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- max_tokens=max_tokens,
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- stream=True,
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- temperature=temperature,
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- top_p=top_p,
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- ):
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- token = message.choices[0].delta.content
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-
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- response += token
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- yield response
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-
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-
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- """
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- For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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- """
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- demo = gr.ChatInterface(
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- respond,
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- additional_inputs=[
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- gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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- gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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- gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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- gr.Slider(
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- minimum=0.1,
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- maximum=1.0,
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- value=0.95,
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- step=0.05,
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- label="Top-p (nucleus sampling)",
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- ),
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- ],
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- )
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-
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-
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- if __name__ == "__main__":
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- demo.launch()
 
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  import gradio as gr
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+ import os
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+ from groq import Groq
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+ from googletrans import Translator
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+ import asyncio
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+ # Function to get recommendations from Groq AI based on user input
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+ def get_opportunities(user_interests, user_skills, user_location):
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+ # Fetch the API key from the environment variable
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+ api_key = "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"
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+
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+ if not api_key:
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+ raise ValueError("API key is missing. Make sure to set the GROQ_API_KEY environment variable.")
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+
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+ # Initialize the Groq client with the API key
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+ client = Groq(api_key=api_key)
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+
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+ # Construct the query
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+ 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."
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+
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+ # Request to Groq API
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+ response = client.chat.completions.create(
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+ messages=[{"role": "user", "content": query}],
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+ model="llama-3.3-70b-versatile",
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+ )
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+
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+ return response.choices[0].message.content
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+ # Function to translate text into the selected language (async version)
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+ async def translate_text(text, target_language):
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+ translator = Translator()
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+ translated = await translator.translate(text, dest=target_language)
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+ return translated.text
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+ # Chatbot interface function
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+ def chatbot(input_text, interests, skills, location, language):
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+ if interests and skills and location:
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+ # Fetch recommendations using the Groq API
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+ opportunities = get_opportunities(interests, skills, location)
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+
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+ # Run the async translate function and get the translated text
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+ translated_opportunities = asyncio.run(translate_text(opportunities, language))
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+
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+ return translated_opportunities
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+ else:
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+ return "Please fill all fields: Interests, Skills, and Location."
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+ # Gradio interface components
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+ with gr.Blocks() as demo:
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+ gr.Markdown("# AI-Powered Opportunity Finder for Youth")
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+
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+ with gr.Row():
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+ interests = gr.Textbox(label="Your Interests (e.g., AI, Robotics, Software Engineering):", placeholder="Enter your interests here...")
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+ skills = gr.Textbox(label="Your Skills (e.g., Python, Data Science, Web Development):", placeholder="Enter your skills here...")
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+ location = gr.Textbox(label="Your Location (e.g., Gujrat, Pakistan):", placeholder="Enter your location here...")
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+
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+ language = gr.Dropdown(label="Select your preferred language:", choices=["English", "Spanish", "French", "German", "Italian", "Chinese", "Japanese", "Urdu"], value="English")
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+
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+ with gr.Chatbot() as chatbot_output:
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+ submit_button = gr.Button("Find Opportunities")
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+ submit_button.click(chatbot, inputs=[gr.Textbox(), interests, skills, location, language], outputs=chatbot_output)
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+ # Launch the app
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+ demo.launch()