Delete gradio.py

gradio.py

@@ -1,65 +0,0 @@
-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()