Update app.py

app.py

@@ -2,75 +2,148 @@ import gradio as gr
 from llama_cpp import Llama
 from huggingface_hub import hf_hub_download
 
-# Download the model
-model_name = "Mykes/med_tinyllama_gguf"
-filename = "unsloth.Q4_K_M.gguf"
-model_path = hf_hub_download(repo_id=model_name, filename=filename)
-
-# Initialize the model
-# model = Llama(model_path=model_path, n_ctx=2048, n_threads=4, n_batch=32, use_mmap=True, use_mlock=True, rope_freq_base=10000, rope_freq_scale=1.0)
-model = Llama(model_path=model_path, n_ctx=256, n_threads=2, n_batch=8, use_mlock=True)
-# def preload_model(model, preload_tokens=1024):
-#     # Dummy call to load model into RAM by accessing parts of it
-#     try:
-#         dummy_input = " " * preload_tokens
-#         _ = model(dummy_input, max_tokens=1)
-#         print("Model preloaded into RAM.")
-#     except Exception as e:
-#         print(f"Error preloading model: {e}")
-
-# # Preload the model into RAM
-# preload_model(model)
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    history = history[-3:]
-    # Construct the prompt
-    prompt = f"<s>{system_message}\n\n"
-    for user_msg, assistant_msg in history:
-        prompt += f"<|user|>{user_msg}<|end|></s> <|assistant|>{assistant_msg}<|end|></s>"
-    prompt += f"<|user|>{message}<|end|></s> <|assistant|>"
-
-    # Generate response
-    response = ""
-    for token in model(
-        prompt,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        stream=True,
-        stop=["<|end|>", "</s>"]
+def create_responder(model):
+    def respond(
+        message,
+        history: list[tuple[str, str]],
+        system_message,
+        max_tokens,
+        temperature,
+        top_p,
     ):
-        response += token['choices'][0]['text']
-        yield response.strip()
-
-# Create the Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    undo_btn="Отменить",
-    clear_btn="Очистить",
-    additional_inputs=[
-        # gr.Textbox(value="You are a friendly medical assistant.", label="System message"),
-        gr.Textbox(value="", label="System message"),
-        gr.Slider(minimum=128, maximum=4096, value=2048, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.9,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-    title="Med TinyLlama Chat",
-    description="Chat with the Med TinyLlama model for medical information.",
-)
+        history = history[-3:]
+        # Construct the prompt
+        prompt = f"<s>{system_message}\n\n"
+        for user_msg, assistant_msg in history:
+            prompt += f"<|user|>{user_msg}<|end|></s> <|assistant|>{assistant_msg}<|end|></s>"
+        prompt += f"<|user|>{message}<|end|></s> <|assistant|>"
+
+        # Generate response
+        response = ""
+        for token in model(
+            prompt,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            stream=True,
+            stop=["<|end|>", "</s>"]
+        ):
+            response += token['choices'][0]['text']
+            yield response.strip()
+
+    return respond
 
 if __name__ == "__main__":
+    # Download the model
+    model_name = "Mykes/med_tinyllama_gguf"
+    filename = "unsloth.Q4_K_M.gguf"
+    model_path = hf_hub_download(repo_id=model_name, filename=filename)
+
+    # Initialize the model
     model = Llama(model_path=model_path, n_ctx=256, n_threads=2, n_batch=8, use_mlock=True)
-    demo.launch()
+
+    # Create a responder function with the model
+    respond = create_responder(model)
+
+    # Create the Gradio interface
+    demo = gr.ChatInterface(
+        respond,
+        undo_btn="Отменить",
+        clear_btn="Очистить",
+        additional_inputs=[
+            gr.Textbox(value="", label="System message"),
+            gr.Slider(minimum=128, maximum=4096, value=2048, step=1, label="Max new tokens"),
+            gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.9,
+                step=0.05,
+                label="Top-p (nucleus sampling)"
+            ),
+        ],
+        title="Med TinyLlama Chat",
+        description="Chat with the Med TinyLlama model for medical information.",
+    )
+
+    demo.launch()
+
+
+
+
+
+# import gradio as gr
+# from llama_cpp import Llama
+# from huggingface_hub import hf_hub_download
+
+# # Download the model
+# model_name = "Mykes/med_tinyllama_gguf"
+# filename = "unsloth.Q4_K_M.gguf"
+# model_path = hf_hub_download(repo_id=model_name, filename=filename)
+
+# # Initialize the model
+# # model = Llama(model_path=model_path, n_ctx=2048, n_threads=4, n_batch=32, use_mmap=True, use_mlock=True, rope_freq_base=10000, rope_freq_scale=1.0)
+# model = Llama(model_path=model_path, n_ctx=256, n_threads=2, n_batch=8, use_mlock=True)
+# # def preload_model(model, preload_tokens=1024):
+# #     # Dummy call to load model into RAM by accessing parts of it
+# #     try:
+# #         dummy_input = " " * preload_tokens
+# #         _ = model(dummy_input, max_tokens=1)
+# #         print("Model preloaded into RAM.")
+# #     except Exception as e:
+# #         print(f"Error preloading model: {e}")
+
+# # # Preload the model into RAM
+# # preload_model(model)
+# def respond(
+#     message,
+#     history: list[tuple[str, str]],
+#     system_message,
+#     max_tokens,
+#     temperature,
+#     top_p,
+# ):
+#     history = history[-3:]
+#     # Construct the prompt
+#     prompt = f"<s>{system_message}\n\n"
+#     for user_msg, assistant_msg in history:
+#         prompt += f"<|user|>{user_msg}<|end|></s> <|assistant|>{assistant_msg}<|end|></s>"
+#     prompt += f"<|user|>{message}<|end|></s> <|assistant|>"
+
+#     # Generate response
+#     response = ""
+#     for token in model(
+#         prompt,
+#         max_tokens=max_tokens,
+#         temperature=temperature,
+#         top_p=top_p,
+#         stream=True,
+#         stop=["<|end|>", "</s>"]
+#     ):
+#         response += token['choices'][0]['text']
+#         yield response.strip()
+
+# # Create the Gradio interface
+# demo = gr.ChatInterface(
+#     respond,
+#     undo_btn="Отменить",
+#     clear_btn="Очистить",
+#     additional_inputs=[
+#         # gr.Textbox(value="You are a friendly medical assistant.", label="System message"),
+#         gr.Textbox(value="", label="System message"),
+#         gr.Slider(minimum=128, maximum=4096, value=2048, step=1, label="Max new tokens"),
+#         gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.9,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+#     title="Med TinyLlama Chat",
+#     description="Chat with the Med TinyLlama model for medical information.",
+# )
+
+# if __name__ == "__main__":
+#     demo.launch()