Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load your model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Behathenimro/mediQ-chat")
+model = AutoModelForCausalLM.from_pretrained("Behathenimro/mediQ-chat")
+model.eval()
+
+# Define chat function
+def mediq_chat(message, history=[]):
+    # You can optionally join past conversation to provide more context
+    input_ids = tokenizer.encode(message, return_tensors="pt")
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            max_new_tokens=200,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9
+        )
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Create the Gradio chat interface
+gr.ChatInterface(
+    fn=mediq_chat,
+    title="🧠 MediQ - Clinical Reasoning Chatbot",
+    description="Ask clinical questions. The model will respond based on its training.",
+    theme="default"
+).launch()