Added model to app.py

app.py

@@ -1,63 +1,63 @@
-import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, GPT2TokenizerFast
-
-# Load fine-tuned model
-model_path = "../models/recipe-gpt"
-tokenizer = GPT2TokenizerFast.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(model_path)
-model.eval()
-
-# Device setup
-device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
-model.to(device)
-
-def generate(ingredient_text, temperature, top_k, top_p, max_length):
-    # Format ingredients into a list
-    ingredients = [line.strip("- ").strip() for line in ingredient_text.strip().splitlines() if line.strip()]
-    
-    prompt = "<start>\nIngredients:\n"
-    for ing in ingredients:
-        prompt += f"- {ing}\n"
-    prompt += "Directions:\n"
-
-    inputs = tokenizer(prompt, return_tensors="pt")
-    input_ids = inputs.input_ids.to(device)
-    attention_mask = inputs.attention_mask.to(device)
-
-    with torch.no_grad():
-        output_ids = model.generate(
-            input_ids,
-            attention_mask=attention_mask,
-            do_sample=True,
-            temperature=temperature,
-            top_k=top_k,
-            top_p=top_p,
-            max_length=max_length,
-            eos_token_id=tokenizer.convert_tokens_to_ids("<end>")
-        )
-
-    generated = tokenizer.decode(output_ids[0], skip_special_tokens=False)
-
-    if "Directions:" in generated:
-        generated = generated.split("Directions:")[1]
-    if "<end>" in generated:
-        generated = generated.split("<end>")[0]
-
-    return generated.strip()
-
-iface = gr.Interface(
-    fn=generate,
-    inputs=[
-        gr.Textbox(lines=8, label="Ingredients (one per line)"),
-        gr.Slider(minimum=0.5, maximum=1.5, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0, maximum=100, value=40, step=5, label="Top-k"),
-        gr.Slider(minimum=0.5, maximum=1.0, value=0.9, step=0.05, label="Top-p"),
-        gr.Slider(minimum=50, maximum=150, value=120, step=10, label="Recipe Length"),
-    ],
-    outputs=gr.Textbox(lines=12, label="Generated Recipe Directions"),
-    title="Recipe-GPT",
-    description="Enter a list of ingredients to generate step-by-step cooking directions. Adjust the sliders for more or less creativity."
-)
-
-iface.launch()
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, GPT2TokenizerFast
+
+# Load fine-tuned model
+model_path = "saksh-d/recipe-gpt"
+tokenizer = GPT2TokenizerFast.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+model.eval()
+
+# Device setup
+device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
+model.to(device)
+
+def generate(ingredient_text, temperature, top_k, top_p, max_length):
+    # Format ingredients into a list
+    ingredients = [line.strip("- ").strip() for line in ingredient_text.strip().splitlines() if line.strip()]
+    
+    prompt = "<start>\nIngredients:\n"
+    for ing in ingredients:
+        prompt += f"- {ing}\n"
+    prompt += "Directions:\n"
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs.input_ids.to(device)
+    attention_mask = inputs.attention_mask.to(device)
+
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids,
+            attention_mask=attention_mask,
+            do_sample=True,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            max_length=max_length,
+            eos_token_id=tokenizer.convert_tokens_to_ids("<end>")
+        )
+
+    generated = tokenizer.decode(output_ids[0], skip_special_tokens=False)
+
+    if "Directions:" in generated:
+        generated = generated.split("Directions:")[1]
+    if "<end>" in generated:
+        generated = generated.split("<end>")[0]
+
+    return generated.strip()
+
+iface = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(lines=8, label="Ingredients (one per line)"),
+        gr.Slider(minimum=0.5, maximum=1.5, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0, maximum=100, value=40, step=5, label="Top-k"),
+        gr.Slider(minimum=0.5, maximum=1.0, value=0.9, step=0.05, label="Top-p"),
+        gr.Slider(minimum=50, maximum=150, value=120, step=10, label="Recipe Length"),
+    ],
+    outputs=gr.Textbox(lines=12, label="Generated Recipe Directions"),
+    title="Recipe-GPT",
+    description="Enter a list of ingredients to generate step-by-step cooking directions. Adjust the sliders for more or less creativity."
+)
+
+iface.launch()