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Running
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Zero
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import os
import gradio as gr
import google.generativeai as genai
"""**How to get Google Gemini API Key?**
- Go to https://aistudio.google.com/app/api-keys
- Click "Create API Key"
- Copy the API Key for your use
"""
GEMINI_API_KEY="AIzaSyBg1CYTTOfWBrOzgxBhBLqHjujx7qVurrM"
genai.configure(api_key=GEMINI_API_KEY)
"""
- Similar to Gemini Model we can also use HuggingFace Transformer Models.
- Reference links: https://python.langchain.com/docs/integrations/providers/huggingface , https://python.langchain.com/docs/integrations/llms/huggingface_hub.html
"""
# from langchain.llms import HuggingFacePipeline
# hf = HuggingFacePipeline.from_model_id(
# model_id="gpt2",
# task="text-generation",)
# Initialize Gemini model
gemini_model = genai.GenerativeModel('gemini-1.5-flash')
# Custom LLM wrapper for Gemini
class GeminiLLM:
def __init__(self, model):
self.model = model
self.memory_history = []
def predict(self, user_message):
# Build conversation context
full_prompt = "You are a helpful assistant to answer user queries.\n"
for msg in self.memory_history:
full_prompt += f"{msg}\n"
full_prompt += f"User: {user_message}\nChatbot:"
# Generate response
response = self.model.generate_content(full_prompt)
answer = response.text
# Update memory
self.memory_history.append(f"User: {user_message}")
self.memory_history.append(f"Chatbot: {answer}")
# Keep only last 10 exchanges
if len(self.memory_history) > 20:
self.memory_history = self.memory_history[-20:]
return answer
llm_chain = GeminiLLM(gemini_model)
def get_text_response(user_message,history):
response = llm_chain.predict(user_message = user_message)
return response
demo = gr.ChatInterface(get_text_response, examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
if __name__ == "__main__":
demo.launch(debug=True) #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
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