Optimized requirements and app script

app.py

@@ -2,27 +2,18 @@ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 def load_model():
-    try:
-        model_name = "internistai/base-7b-v0.2"
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-        return model, tokenizer
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        return None, None
+    model_name = "internistai/base-7b-v0.2"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return model, tokenizer
 
 def chat(message, model, tokenizer):
-    try:
-        inputs = tokenizer(message, return_tensors="pt")
-        outputs = model.generate(**inputs)
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return response
-    except Exception as e:
-        return f"Error generating response: {e}"
+    inputs = tokenizer(message, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
 
 model, tokenizer = load_model()
-if model is None or tokenizer is None:
-    print("Failed to load model or tokenizer. Please check the configuration.")
 
 iface = gr.Interface(fn=lambda message: chat(message, model, tokenizer), inputs="text", outputs="text")
 iface.launch()

requirements.txt

@@ -1,4 +1,4 @@
 gradio
 transformers
-torch==1.9.0  # Specify a version if newer versions are causing issues
+torch
 sentencepiece