Add application files

.history/app_20250731152751.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Charger ton modèle et tokenizer depuis le Hub
+model_name = "eric-houzelle/mermaidGPT"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_mermaid(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=100)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+
+iface = gr.Interface(
+    fn=generate_mermaid,
+    inputs=gr.Textbox(lines=2, placeholder="Describe your diagram in English..."),
+    outputs=gr.Code(language="markdown"),
+    title="🧠 mermaidGPT — Mermaid Generator",
+    description="Enter a short English instruction and get a Mermaid.js diagram code."
+)
+
+iface.launch()

.history/app_20250731171037.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Charger ton modèle et tokenizer depuis le Hub
+model_name = "Houzeric/text-to-mermaid"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_mermaid(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=100)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+
+iface = gr.Interface(
+    fn=generate_mermaid,
+    inputs=gr.Textbox(lines=2, placeholder="Describe your diagram in English..."),
+    outputs=gr.Code(language="markdown"),
+    title="🧠 mermaidGPT — Mermaid Generator",
+    description="Enter a short English instruction and get a Mermaid.js diagram code."
+)
+
+iface.launch()

.history/app_20250731171040.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "Houzeric/text-to-mermaid"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_mermaid(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=100)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+
+iface = gr.Interface(
+    fn=generate_mermaid,
+    inputs=gr.Textbox(lines=2, placeholder="Describe your diagram in English..."),
+    outputs=gr.Code(language="markdown"),
+    title="🧠 mermaidGPT — Mermaid Generator",
+    description="Enter a short English instruction and get a Mermaid.js diagram code."
+)
+
+iface.launch()

.history/requirements_20250731152806.txt

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+gradio
+transformers
+torch

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "Houzeric/text-to-mermaid"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_mermaid(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=100)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+
+iface = gr.Interface(
+    fn=generate_mermaid,
+    inputs=gr.Textbox(lines=2, placeholder="Describe your diagram in English..."),
+    outputs=gr.Code(language="markdown"),
+    title="🧠 mermaidGPT — Mermaid Generator",
+    description="Enter a short English instruction and get a Mermaid.js diagram code."
+)
+
+iface.launch()

requirements.txt

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+gradio
+transformers
+torch