Update app.js

app.js

@@ -1,57 +1,53 @@
 import gradio as gr
-import requests
-
-# Replace with your model endpoint or HF Inference API
-MODEL_URL = "https://api-inference.huggingface.co/models/YOUR_MODEL"
-API_KEY = "YOUR_KEY"
-
-headers = {"Authorization": f"Bearer {API_KEY}"}
-
-def generate_react(prompt):
-    if not prompt.strip():
-        return "// No prompt provided."
-
-    payload = {
-        "inputs": f"Write React code for: {prompt}",
-        "parameters": {"max_new_tokens": 400}
-    }
-
-    try:
-        response = requests.post(MODEL_URL, headers=headers, json=payload)
-        data = response.json()
-
-        if isinstance(data, dict) and "error" in data:
-            return f"// Model error: {data['error']}"
-
-        return data[0]["generated_text"]
-    except Exception as e:
-        return f"// Error: {str(e)}"
-
-
-with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
-    gr.Markdown(
-        """
-        # ⚡ GhosTech React Code Generator  
-        Type what you want and generate clean React components, hooks, utilities, or full pages.
-        """
-    )
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            prompt = gr.Textbox(
-                label="Describe the React code you want",
-                placeholder="Example: A React component that fetches weather data and displays it...",
-                lines=6,
-            )
-            generate_btn = gr.Button("Generate React Code", variant="primary")
-
-        with gr.Column(scale=1):
-            output = gr.Code(
-                label="Generated React Code",
-                language="javascript",
-                value="// Your generated code will appear here."
-            )
-
-    generate_btn.click(generate_react, inputs=prompt, outputs=output)
-
-demo.launch()
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Choose a lightweight, open model
+model_name = "mistralai/Mistral-7B-Instruct-v0.2"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+
+def chat(history, message):
+    # Build conversation text
+    prompt = ""
+    for user, bot in history:
+        prompt += f"User: {user}\nAssistant: {bot}\n"
+    prompt += f"User: {message}\nAssistant:"
+
+    output = pipe(prompt)[0]["generated_text"]
+    reply = output.split("Assistant:")[-1].strip()
+
+    history.append((message, reply))
+    return history, ""
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔥 My Chatbot")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Say something")
+    clear = gr.Button("Clear chat")
+
+    state = gr.State([])
+
+    def respond(message, history):
+        if history is None:
+            history = []
+        return chat(history, message)
+
+    msg.submit(respond, [msg, chatbot], [chatbot, msg])
+    clear.click(lambda: ([], ""), None, [chatbot, msg])
+
+demo.launch()