rashid01/tracker

1from transformers import pipeline2import requests3 4# Replace with your actual LangSmith API key5LANGSMITH_API_KEY = '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'6LANGSMITH_ENDPOINT = 'https://smith.langchain.com/o/b7d2cb3f-e589-52bb-9b8a-2e8483e4ee8d/tailor'  # Make sure this is the correct endpoint7 8# Initialize the Hugging Face text generation pipeline with BlenderBot9conversational_pipeline = pipeline('text-generation', model='facebook/blenderbot-3B')10 11def tailor_with_langsmith(model_data):12    headers = {13        'Authorization': f'Bearer {LANGSMITH_API_KEY}',14        'Content-Type': 'application/json'15    }16    data = {17        'model_data': model_data18    }19    response = requests.post(LANGSMITH_ENDPOINT, json=data, headers=headers)20    response.raise_for_status()21    return response.json()22 23def create_custom_conversation(prompt):24    # Step 1: Get response from Hugging Face model25    hf_response = conversational_pipeline(prompt, max_length=50)  # Adjust max_length as needed26    hf_reply = hf_response[0]['generated_text']27 28    # Step 2: Tailor the response using LangSmith29    tailored_response = tailor_with_langsmith({'model_data': hf_reply})30    tailored_reply = tailored_response.get('tailored_reply', '')31 32    return tailored_reply33 34if __name__ == '__main__':35    user_prompt = "Tell me about the latest advancements in AI."36    response = create_custom_conversation(user_prompt)37    print("Tailored Response:", response)38