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Update app.py
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app.py
CHANGED
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@@ -14,14 +14,20 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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class BasicAgent:
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def __init__(self):
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print("
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# Load tokenizer and model
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self.tokenizer = AutoTokenizer.from_pretrained(model_id)
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self.model = AutoModelForCausalLM.from_pretrained(
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# Create
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self.pipeline = pipeline(
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"text-generation",
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model=self.model,
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@@ -33,22 +39,25 @@ class BasicAgent:
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print(f"Agent received question: {question[:50]}...")
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try:
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output = self.pipeline(
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prompt,
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max_new_tokens=
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)
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full_response = output[0]["generated_text"]
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answer = full_response.split("
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return answer
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except Exception as e:
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print(f"❌
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return f"❌ Model Error: {str(e)}"
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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Fetches all questions, runs the BasicAgent on them, submits all answers,
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class BasicAgent:
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def __init__(self):
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print("Mistral Agent loading on CPU...")
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model_id = "mistralai/Mistral-7B-Instruct-v0.1"
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# Load tokenizer and model
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self.tokenizer = AutoTokenizer.from_pretrained(model_id)
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self.model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto", # Will default to CPU
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low_cpu_mem_usage=True, # Helps a bit
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torch_dtype="auto"
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)
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# Create pipeline (CPU-only)
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self.pipeline = pipeline(
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"text-generation",
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model=self.model,
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print(f"Agent received question: {question[:50]}...")
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try:
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# Format with instruction template
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prompt = f"<s>[INST] {question.strip()} [/INST]"
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output = self.pipeline(
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prompt,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.7,
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top_p=0.95
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)
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full_response = output[0]["generated_text"]
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answer = full_response.split("[/INST]")[-1].strip()
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return answer
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except Exception as e:
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print(f"❌ Mistral error: {e}")
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return f"❌ Model Error: {str(e)}"
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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Fetches all questions, runs the BasicAgent on them, submits all answers,
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