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Update app.py
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app.py
CHANGED
@@ -34,7 +34,7 @@ if "follow_up" not in st.session_state:
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st.session_state.follow_up = ""
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# β
Initialize Hugging Face Model (Explicitly Set to CPU/GPU)
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def get_llm_hf_inference(model_id="meta-llama/Llama-2-7b-chat-hf", max_new_tokens=
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#mistralai/Mistral-7B-Instruct-v0.3
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return HuggingFaceEndpoint(
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repo_id=model_id,
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@@ -91,7 +91,7 @@ def generate_follow_up(user_text):
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"Ensure it's concise and structured exactly as requested without extra commentary."
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)
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hf = get_llm_hf_inference(max_new_tokens=30, temperature=0.
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output = hf.invoke(input=prompt_text).strip()
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# β
Extract the relevant part using regex to remove unwanted symbols or truncations
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@@ -104,7 +104,7 @@ def generate_follow_up(user_text):
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return cleaned_output
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# β
Main Response Function
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def get_response(system_message, chat_history, user_text, max_new_tokens=
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action = predict_action(user_text)
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# β
Handle NASA-Specific Queries
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st.session_state.follow_up = ""
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# β
Initialize Hugging Face Model (Explicitly Set to CPU/GPU)
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def get_llm_hf_inference(model_id="meta-llama/Llama-2-7b-chat-hf", max_new_tokens=800, temperature=0.8):
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#mistralai/Mistral-7B-Instruct-v0.3
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return HuggingFaceEndpoint(
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repo_id=model_id,
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"Ensure it's concise and structured exactly as requested without extra commentary."
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)
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hf = get_llm_hf_inference(max_new_tokens=30, temperature=0.8) # π₯ Lower temp for consistency
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output = hf.invoke(input=prompt_text).strip()
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# β
Extract the relevant part using regex to remove unwanted symbols or truncations
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return cleaned_output
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# β
Main Response Function
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def get_response(system_message, chat_history, user_text, max_new_tokens=800):
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action = predict_action(user_text)
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# β
Handle NASA-Specific Queries
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