Upload app.py

app.py

@@ -1,64 +1,135 @@
+import requests
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Minimal Configuration
+API_URL = "http://127.0.0.1:8080/v1/chat/completions"
 
+# Store the last prompt
+last_prompt = ""
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def generate(prompt):
+    """Generation function with prompt storage"""
+    global last_prompt
+    last_prompt = prompt
+    try:
+        response = requests.post(
+            API_URL,
+            json={
+                "model": "TinyLlama-1.1B-Chat-v1.0",
+                "messages": [{"role": "user", "content": prompt}],
+                "max_tokens": 256
+            },
+            timeout=100
+        )
+        return response.json()['choices'][0]['message']['content']
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-    messages.append({"role": "user", "content": message})
 
-    response = ""
+def regenerate():
+    """Regenerate response using last prompt"""
+    if last_prompt:
+        return generate(last_prompt)
+    return "No previous prompt to regenerate"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
 
-        response += token
-        yield response
+# Styled UI with improved organization
+with gr.Blocks(title="Karlson Achegeba GPT", theme=gr.themes.Soft(primary_hue="blue")) as app:
+    with gr.Column(elem_classes=["center-container"]):
+        # Header Section
+        gr.Markdown(
+            """<div style='text-align: center;'>
+            <h1 style='color: #2563eb; margin-bottom: 0;'>Karlson Achegeba GPT</h1>
+            <p style='color: #64748b;'>Powered by TinyLlama</p>
+            </div>"""
+        )
 
+        # Chat Interface
+        with gr.Group(elem_classes=["chat-container"]):
+            # Input Section
+            with gr.Column():
+                prompt = gr.Textbox(
+                    placeholder="Type your message here...",
+                    lines=5,
+                    label="Your Message",
+                    elem_classes=["input-box"]
+                )
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+                # Action Buttons
+                with gr.Row():
+                    submit = gr.Button(
+                        "Generate Response",
+                        variant="primary",
+                        elem_classes=["action-button"]
+                    )
+                    regenerate_btn = gr.Button(
+                        "Regenerate",
+                        variant="secondary",
+                        elem_classes=["action-button"]
+                    )
+                    clear_btn = gr.Button(
+                        "Clear",
+                        variant="secondary",
+                        elem_classes=["action-button"]
+                    )
 
+            # Output Section
+            output = gr.Textbox(
+                label="AI Response",
+                interactive=False,
+                lines=10,
+                elem_classes=["output-box"]
+            )
 
-if __name__ == "__main__":
-    demo.launch()
+    # Event Handlers
+    submit.click(fn=generate, inputs=prompt, outputs=output)
+    regenerate_btn.click(fn=regenerate, outputs=output)
+    clear_btn.click(fn=lambda: ("", ""), outputs=[prompt, output])  # Clears both input and output
+
+    # Custom CSS
+    app.css = """
+    .center-container {
+        max-width: 800px;
+        margin: auto;
+        padding: 20px;
+    }
+    .chat-container {
+        display: flex;
+        flex-direction: column;
+        gap: 20px;
+    }
+    .input-box, .output-box {
+        border-radius: 8px;
+        border: 1px solid #e2e8f0;
+        padding: 12px;
+    }
+    .input-box textarea, .output-box textarea {
+        font-size: 16px !important;
+    }
+    .action-button {
+        border-radius: 8px !important;
+        padding: 8px 16px !important;
+        min-width: 120px;
+    }
+    .action-button:not(.secondary) {
+        background: #2563eb !important;
+        color: white !important;
+    }
+    .action-button.secondary {
+        border: 1px solid #2563eb !important;
+        color: #2563eb !important;
+    }
+    .group {
+        border: 1px solid #e2e8f0;
+        border-radius: 12px;
+        padding: 20px;
+        background: white;
+        box-shadow: 0 2px 8px rgba(0,0,0,0.05);
+    }
+    .row {
+        gap: 10px;
+    }
+    """
+
+app.launch(server_port=7860 , share = True)