Create app.py

app.py

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+import gradio as gr
+import requests
+
+# Hugging Face API details
+API_URL = "https://api-inference.huggingface.co/models/nasa-impact/nasa-smd-ibm-v0.1"
+headers = {"Authorization": "Bearer HF_TOKEN"}
+
+def query_huggingface(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+# Function to generate response
+def generate_response(user_message, system_message="You are a helpful assistant.", history=[]):
+    messages = [{"role": "system", "content": system_message}]
+
+    for user, assistant in history:
+        if user:
+            messages.append({"role": "user", "content": user})
+        if assistant:
+            messages.append({"role": "assistant", "content": assistant})
+
+    messages.append({"role": "user", "content": user_message})
+
+    payload = {
+        "inputs": messages,
+        "parameters": {
+            "max_tokens": 50,
+            "temperature": 0.7,
+            "top_p": 0.9,
+            "stream": False
+        }
+    }
+
+    response = query_huggingface(payload)
+    output = response.get('generated_text', 'No response')
+
+    return output
+
+# Gradio interface
+def gradio_interface(user_message, system_message, history):
+    response = generate_response(user_message, system_message, history)
+    history.append((user_message, response))
+    return history, history
+
+# Define Gradio blocks
+with gr.Blocks() as demo:
+    system_message = gr.Textbox(label="System Message", value="You are a helpful assistant.")
+    user_message = gr.Textbox(label="Your Message")
+    chatbot = gr.Chatbot(label="Chat History")
+    state = gr.State([])
+    submit_button = gr.Button("Submit")
+
+    submit_button.click(gradio_interface, inputs=[user_message, system_message, state], outputs=[chatbot, state])
+
+# Launch the interface
+demo.launch()