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README.md

@@ -1,12 +1,17 @@
----
-title: SentimentProfAI
-emoji: 😻
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 5.34.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SentimentProfAI - Gradio Space
+
+Questo Space esegue sentiment analysis su recensioni di film usando TinyLlama + LoRA adapter.
+
+## Come funziona
+- Inserisci una recensione di un film
+- Il modello risponde con label (positive/negative) e una breve motivazione
+
+## Modello
+- Base: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+- Adapter: riccardomusmeci/SentimentProfAI
+
+## Dipendenze
+Vedi `requirements.txt`.
+
+## Autore
+Riccardo Musmeci

gradio.py

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+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import json
+
+MODEL_ID = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+ADAPTER_REPO = "riccardomusmeci/SentimentProfAI"
+
+SYSTEM_PROMPT = """<|system|>
+Analyze the sentiment of the following movie review and label it as positive or negative.
+Provide ONLY an output in JSON format with two fields:
+- "label": "positive" or "negative"
+- "reasoning": a brief explanation of your classification
+
+Do not add any other text after the JSON.</s>"""
+
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16 if device in ["cuda", "mps"] else torch.float32,
+)
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER_REPO)
+model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
+model.to(device)
+model.eval()
+
+def sentiment_analysis(review_text):
+    prompt = f"{SYSTEM_PROMPT}<|user|>\n{review_text}</s>\n<|assistant|>\n"
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            do_sample=False,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    # Estrai solo la parte JSON dalla risposta
+    try:
+        start = response.index('{')
+        end = response.rindex('}') + 1
+        json_str = response[start:end]
+        sentiment_json = json.loads(json_str)
+        label = sentiment_json.get("label", "")
+        reasoning = sentiment_json.get("reasoning", "")
+    except Exception:
+        label = "Errore"
+        reasoning = f"Impossibile estrarre il JSON. Output grezzo: {response}"
+    return label, reasoning
+
+iface = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=gr.Textbox(label="Movie Review"),
+    outputs=[
+        gr.Textbox(label="Label"),
+        gr.Textbox(label="Reasoning")
+    ],
+    title="Sentiment Analysis ProfAI",
+    description="Analizza la recensione di un film e restituisce il sentiment (positivo/negativo) e la motivazione."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+transformers>=4.40.0
+peft>=0.10.0
+torch
+accelerate
+gradio