Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the Hugging Face model and tokenizer
+model_name = "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+
+# Define the function to process user input
+def generate_response(input_text):
+    try:
+        # Tokenize the input text
+        inputs = tokenizer(input_text, return_tensors="pt")
+        
+        # Generate a response using the model
+        outputs = model.generate(
+            inputs["input_ids"],
+            max_length=256,  # Limit the output length
+            num_return_sequences=1,  # Generate a single response
+            temperature=0.7,  # Adjust for creativity vs. determinism
+            top_p=0.9,  # Nucleus sampling
+            top_k=50  # Top-k sampling
+        )
+        
+        # Decode and return the generated text
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create a Gradio interface with API enabled
+iface = gr.Interface(
+    fn=generate_response,
+    inputs="text",
+    outputs="text",
+    title="ContactDoctor Medical Assistant",
+    description="Provide input symptoms or queries and get AI-powered medical advice.",
+    enable_api=True  # Enables API for external calls
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()