Upload app.py

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Initialize the Hugging Face Inference Client
+# No API key needed for public models, but you can add one for better rate limits
+client = InferenceClient()
+
+def chatbot(question, model, temperature):
+    """Send the question to HuggingFace model using the Inference API.
+    
+    This function sends your question to a HuggingFace model and returns the response.
+    """
+    if not question:
+        return "Please enter a question."
+    
+    try:
+        # Call HuggingFace Inference API with the selected model
+        response = client.chat_completion(
+            model=model,
+            messages=[{"role": "user", "content": question}],
+            temperature=float(temperature),
+            max_tokens=500
+        )
+        
+        # Extract the response content
+        content = response.choices[0].message.content
+        
+        # Include model and temperature information in the returned text
+        return f"Model: {model}\nTemperature: {temperature}\n\n{content}"
+    
+    except Exception as e:
+        return f"Error: {str(e)}\n\nPlease try again or select a different model."
+
+
+def main():
+    # Build a simple Gradio app with a left-side control panel (model + temperature)
+    with gr.Blocks(title="HuggingFace Chatbot") as demo:
+        gr.Markdown("# 🤗 HuggingFace Chatbot")
+        gr.Markdown("Ask questions and get responses from various HuggingFace AI models!")
+        
+        with gr.Row():
+            # Left column: grouped controls (model dropdown + temperature slider)
+            with gr.Column(scale=1):
+                gr.Markdown("**Model & Settings**")
+                model_dropdown = gr.Dropdown(
+                    label="Model",
+                    choices=[
+                        "meta-llama/Llama-3.2-3B-Instruct",
+                        "microsoft/Phi-3-mini-4k-instruct",
+                        "mistralai/Mistral-7B-Instruct-v0.3",
+                        "HuggingFaceH4/zephyr-7b-beta"
+                    ],
+                    value="meta-llama/Llama-3.2-3B-Instruct",
+                )
+                temp_slider = gr.Slider(
+                    label="Temperature",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.01,
+                    value=0.7,
+                    info="Higher values make output more random"
+                )
+
+            # Right column: input and output
+            with gr.Column(scale=3):
+                question = gr.Textbox(
+                    label="Question", 
+                    lines=3, 
+                    placeholder="Type your message here..."
+                )
+                submit = gr.Button("Submit", variant="primary")
+                output = gr.Textbox(label="Response", lines=15)
+
+        # Wire up the button to call the chatbot function with model and temperature
+        submit.click(
+            fn=chatbot, 
+            inputs=[question, model_dropdown, temp_slider], 
+            outputs=output
+        )
+        
+        # Also allow pressing Enter to submit
+        question.submit(
+            fn=chatbot,
+            inputs=[question, model_dropdown, temp_slider],
+            outputs=output
+        )
+
+    demo.launch()
+
+
+if __name__ == "__main__":
+    main()
+