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app.py

@@ -0,0 +1,33 @@
+import json, re, ast, streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model_id = "mistralai/Mistral-7B-Instruct-v0.3"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+gen = pipeline("text-generation", model=model, tokenizer=tok,
+               max_new_tokens=256, do_sample=False, return_full_text=False)
+
+prompt = """Extract skills and knowledge from the text.
+Return JSON: {"SKILL":[...], "KNOWLEDGE":[...]}.
+Text: {text}
+JSON:"""
+
+def extract(text: str):
+    out = gen(prompt.format(text=text))
+    raw = out[0].get("generated_text") or out[0].get("text") or str(out[0])
+    m = re.search(r"\{[\s\S]*\}", raw)
+    data = {}
+    if m:
+        blob = m.group(0)
+        for parser in (json.loads, ast.literal_eval):
+            try:
+                data = parser(blob); break
+            except Exception: pass
+    if not isinstance(data, dict):
+        data = {}
+    return {"SKILL": data.get("SKILL", []), "KNOWLEDGE": data.get("KNOWLEDGE", [])}
+
+st.title("Skill/Knowledge Extractor")
+text = st.text_area("Paste text")
+if st.button("Extract") and text.strip():
+    st.json(extract(text))

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
+transformers
+accelerate
 streamlit