Update app.py

app.py

@@ -8,27 +8,21 @@ response = requests.get(url)
 with open("./model.gguf", mode="wb") as file:
   file.write(response.content)
 
-app = create_app(
-    Settings(
-        n_threads=2,  # set to number of cpu cores
-        model="./model.gguf",
-        embedding=False
-    )
-)
+llm = Llama(model_path="./model.gguf")
+def generate_text(input_text):
+    output = llm(f"Q: {input_text} A:", max_tokens=256, stop=["Q:", "\n"], echo=True)
+    return output['choices'][0]['text']
 
-# Read the content of index.html once and store it in memory
-with open("index.html", "r") as f:
-    content = f.read()
+input_text = gr.inputs.Textbox(lines= 10, label="Enter your input text")
+output_text = gr.outputs.Textbox(label="Output text")
 
+description = "llama.cpp implementation in python [https://github.com/abetlen/llama-cpp-python]"
 
-@app.get("/", response_class=HTMLResponse)
-async def read_items():
-    return content
+examples = [
+    ["What is the capital of France? ", "The capital of France is Paris."],
+    ["Who wrote the novel 'Pride and Prejudice'?", "The novel 'Pride and Prejudice' was written by Jane Austen."],
+    ["What is the square root of 64?", "The square root of 64 is 8."]
+]
 
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app,
-                host="0.0.0.0",
-                port=int("2600")
-                )
+gr.Interface(fn=generate_text, inputs=input_text, outputs=output_text, title="Llama Language Model", description=description, examples=examples).launch()