Create app.py

app.py

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+import gradio as gr
+from langchain_huggingface import HuggingFaceEndpoint
+from secrets import HUGGING_FACE_TOKEN  # Importing the token from a separate file
+
+# Define your HuggingFace endpoint details
+repo_id_1 = "mistralai/Mistral-7B-Instruct-v0.2"
+repo_id_2 = "mistralai/Mistral-7B-Instruct-v0.3"
+
+# Initialize the HuggingFace endpoints
+llm_1 = HuggingFaceEndpoint(repo_id=repo_id_1, max_length=128, temperature=0.7, token=HUGGING_FACE_TOKEN)
+llm_2 = HuggingFaceEndpoint(repo_id=repo_id_2, max_length=128, temperature=0.7, token=HUGGING_FACE_TOKEN)
+
+# Define a function to get responses from both models
+def get_combined_response(prompt):
+    response_1 = llm_1.invoke(prompt)
+    response_2 = llm_2.invoke(prompt)
+    combined_response = f"Model 1 Response: {response_1}\n\nModel 2 Response: {response_2}"
+    return combined_response
+
+# Create a Gradio interface for the combined function
+iface_combined = gr.Interface(fn=get_combined_response, inputs="text", outputs="text", title="Combined Machine Learning Chatbots")
+
+# Launch the Gradio app
+iface_combined.launch()