Update src/insight.py

src/insight.py

@@ -1,34 +1,37 @@
-# insight.py
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-import json
-
-# Load Gemma model from Hugging Face
-model_id = "google/gemma-3n-E4B-it"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-
-def call_llm(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
-    outputs = model.generate(**inputs, max_new_tokens=1024)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-insight_prompt = """
-You are a senior data analyst. You are given a dataset summary and column statistics after cleaning.
-
-Please perform the following:
-1. Describe the structure of the data in natural language.
-2. Mention any interesting patterns or distributions (e.g. most common values, ranges, anomalies).
-3. Derive any basic insights you can (e.g. relationships between columns, high-cardinality features, outliers).
-4. Point out anything surprising or worth further investigation.
-
-Be specific. Don't explain generic EDA steps — interpret the data as if you're preparing a short report.
-
-Column Summary:
-{column_data}
-"""
-
-def generate_insights(column_data):
-    prompt = insight_prompt.format(column_data=json.dumps(column_data, indent=2))
-    return call_llm(prompt)
-
+# insight.py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import json
+import os
+
+
+model_id = "google/gemma-3n-E4B-it"
+hf_token = os.environ.get("HUGGINGFACE_TOKEN")  
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)
+model = AutoModelForCausalLM.from_pretrained(model_id, token=hf_token)
+
+
+def call_llm(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
+    outputs = model.generate(**inputs, max_new_tokens=1024)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+insight_prompt = """
+You are a senior data analyst. You are given a dataset summary and column statistics after cleaning.
+
+Please perform the following:
+1. Describe the structure of the data in natural language.
+2. Mention any interesting patterns or distributions (e.g. most common values, ranges, anomalies).
+3. Derive any basic insights you can (e.g. relationships between columns, high-cardinality features, outliers).
+4. Point out anything surprising or worth further investigation.
+
+Be specific. Don't explain generic EDA steps — interpret the data as if you're preparing a short report.
+
+Column Summary:
+{column_data}
+"""
+
+def generate_insights(column_data):
+    prompt = insight_prompt.format(column_data=json.dumps(column_data, indent=2))
+    return call_llm(prompt)
+