changes for actiongemma

app.py

@@ -1,11 +1,13 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import json
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
+model = AutoModelForCausalLM.from_pretrained("KishoreK/ActionGemma-9B", load_in_4bit=True, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("KishoreK/ActionGemma-9B")
 
 def respond(
     message,
@@ -15,29 +17,77 @@ def respond(
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    task_instruction = """
+You are an expert in composing functions. You are given a question and a set of possible functions. 
+Based on the question, you will need to make one or more function/tool calls to achieve the purpose. 
+If none of the functions can be used, point it out and refuse to answer. 
+If the given question lacks the parameters required by the function, also point it out.
+""".strip()
+
+    get_weather_api = {
+        "name": "get_weather",
+        "description": "Get the current weather for a location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, New York"
+                },
+                "unit": {
+                    "type": "string",
+                    "enum": ["celsius", "fahrenheit"],
+                    "description": "The unit of temperature to return"
+                }
+            },
+            "required": ["location"]
+        }
+    }
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    search_api = {
+        "name": "search",
+        "description": "Search for information on the internet",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "The search query, e.g. 'latest news on AI'"
+                }
+            },
+            "required": ["query"]
+        }
+    }
 
-    messages.append({"role": "user", "content": message})
+    openai_format_tools = [get_weather_api, search_api]
 
-    response = ""
+    def convert_to_xlam_tool(tools):
+        ''''''
+        if isinstance(tools, dict):
+            return {
+                "name": tools["name"],
+                "description": tools["description"],
+                "parameters": {k: v for k, v in tools["parameters"].get("properties", {}).items()}
+            }
+        elif isinstance(tools, list):
+            return [convert_to_xlam_tool(tool) for tool in tools]
+        else:
+            return tools
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    user_query = message
+    tools = openai_format_tools
+    messages = [{
+        "role" : "system",
+        "content" : task_instruction
+    },{
+        "role" : "user",
+        "content" : user_query
+    },{
+        "role": "tools",
+        "content": json.dumps(convert_to_xlam_tool(tools))
+    }]
 
-        response += token
-        yield response
+    return tokenizer.decode(tokenizer.apply_chat_template(messages, add_generation_prompt=True))
 
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
@@ -45,17 +95,10 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Textbox(value="You are an expert in composing functions.", label="System message"),
     ],
+    examples=["अमेरिका के राष्ट्रपति कौन है?"],
+    description="This is ActionGemma, LAM with multi-lingual capabilities. currently this model is prompted with only 2 tools available : get_weather_api and search_api. Integrations for more api's will be coming soon."
 )