Delete app.py

app.py

@@ -1,51 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-# Load model from Hugging Face Hub (replace with your username/model name)
-model_name = "hari7261/TechChat"
-
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# Chat function
-def chat(prompt, max_new_tokens=200, temperature=0.7, top_p=0.9):
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=max_new_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            pad_token_id=tokenizer.eos_token_id
-        )
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# 💬 TechChat - Domain Chatbot")
-    gr.Markdown("Fine-tuned from Mistral-7B for technical Q&A")
-
-    with gr.Row():
-        with gr.Column(scale=3):
-            user_input = gr.Textbox(lines=3, placeholder="Ask me something technical...", label="Your Question")
-            max_tokens = gr.Slider(50, 500, value=200, step=10, label="Max New Tokens")
-            temperature = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
-            top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p Sampling")
-            submit_btn = gr.Button("Generate Answer")
-
-        with gr.Column(scale=5):
-            output_box = gr.Textbox(label="TechChat Response")
-
-    submit_btn.click(
-        fn=chat,
-        inputs=[user_input, max_tokens, temperature, top_p],
-        outputs=output_box
-    )
-
-demo.launch()