Update app.py

app.py

@@ -9,39 +9,30 @@ import requests
 import random
 from gradio_client import Client, file
 
+# Define functions for image captioning, web search, and text extraction
 def generate_caption_instructblip(image_path, question):
     client = Client("hysts/image-captioning-with-blip")
     return client.predict(file(image_path), f"{question}", api_name="/caption")
 
 def extract_text_from_webpage(html_content):
-    """Extracts visible text from HTML content using BeautifulSoup."""
     soup = BeautifulSoup(html_content, 'html.parser')
-    # Remove unwanted tags
     for tag in soup(["script", "style", "header", "footer"]):
         tag.extract()
     return soup.get_text(strip=True)
 
-# Perform a Google search and return the results
 def search(query):
-    """Performs a Google search and returns the results."""
-    term=query
+    term = query
     print(f"Running web search for query: {term}")
     start = 0
     all_results = []
-    # Limit the number of characters from each webpage to stay under the token limit
-    max_chars_per_page = 8000  # Adjust this value based on your token limit and average webpage length
-    
-    with requests.Session() as session: 
-        resp = session.get(  
+    max_chars_per_page = 8000  
+    with requests.Session() as session:
+        resp = session.get(
             url="https://www.google.com/search",
-            headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, 
-                params={
-                    "q": term,
-                    "num": 3,
-                    "udm": 14,
-                },
-                timeout=5,
-                verify=None,
+            headers={"User-Agent": "Mozilla/5.0"},
+            params={"q": term, "num": 3, "udm": 14},
+            timeout=5,
+            verify=None,
         )
         resp.raise_for_status()
         soup = BeautifulSoup(resp.text, "html.parser")
@@ -50,10 +41,9 @@ def search(query):
             link = result.find("a", href=True)
             link = link["href"]
             try:
-                webpage = session.get(link, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, timeout=5,verify=False) 
+                webpage = session.get(link, headers={"User-Agent": "Mozilla/5.0"}, timeout=5, verify=False)
                 webpage.raise_for_status()
                 visible_text = extract_text_from_webpage(webpage.text)
-                        # Truncate text if it's too long
                 if len(visible_text) > max_chars_per_page:
                     visible_text = visible_text[:max_chars_per_page]
                 all_results.append({"link": link, "text": visible_text})
@@ -61,114 +51,43 @@ def search(query):
                 all_results.append({"link": link, "text": None})
     return all_results
 
-
+# Initialize inference clients for different models
 client = InferenceClient("google/gemma-1.1-7b-it")
+client_mixtral = InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
+client_llama = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+
+# Define the main chat function
+def respond(message, history):
+    global messages # Make messages global for persistent storage 
+    messages = [] # Initialize messages list (this gets overwritten each turn)
+    vqa = ""
 
-def respond(
-    message, history
-):
-    messages = []
-    vqa=""
+    # Handle image processing
     if message["files"]:
         try:
-            for image in message["files"]: 
+            for image in message["files"]:
                 vqa += "[CAPTION of IMAGE]  "
                 gr.Info("Analyzing image")
                 vqa += generate_caption_instructblip(image, message["text"])
                 print(vqa)
         except:
             vqa = ""
-            
-            
-        
+
+    # Define function metadata for user interface
     functions_metadata = [
-        {
-            "type": "function",
-            "function": {
-                "name": "web_search",
-                "description": "Search query on google and find latest information.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {
-                            "type": "string",
-                            "description": "web search query",
-                        }
-                    },
-                    "required": ["query"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "general_query",
-                "description": "Reply general query of USER through LLM like you, it does'nt know latest information. But very helpful in general query. Its very powerful LLM. It knows many thing just like you except latest things, or thing that you don't know.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "prompt": {
-                            "type": "string",
-                            "description": "A detailed prompt so that an LLm can understand better, what user wants.",
-                        }
-                    },
-                    "required": ["prompt"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "image_generation",
-                "description": "Generate image for user.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {
-                            "type": "string",
-                            "description": "image generation prompt in detail.",
-                        },
-                        "number_of_image": {
-                            "type": "integer",
-                            "description": "number of images to generate.",
-                        }
-                    },
-                    "required": ["query"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "image_qna",
-                "description": "Answer question asked by user related to image.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {
-                            "type": "string",
-                            "description": "Question by user",
-                        }
-                    },
-                    "required": ["query"],
-                },
-            },
-        }
+        {"type": "function", "function": {"name": "web_search", "description": "Search query on google", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "web search query"}}, "required": ["query"]}}},
+        {"type": "function", "function": {"name": "general_query", "description": "Reply general query of USER", "parameters": {"type": "object", "properties": {"prompt": {"type": "string", "description": "A detailed prompt"}}, "required": ["prompt"]}}},
+        {"type": "function", "function": {"name": "image_generation", "description": "Generate image for user", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "image generation prompt"}, "number_of_image": {"type": "integer", "description": "number of images to generate"}}, "required": ["query"]}}},
+        {"type": "function", "function": {"name": "image_qna", "description": "Answer question asked by user related to image", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Question by user"}}, "required": ["query"]}}},
     ]
 
     message_text = message["text"]
-    
 
-    client_mixtral = InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
-    client_llama = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
-    generate_kwargs = dict( max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False )
+    # Append user messages and system instructions to the messages list
+    messages.append({"role": "user", "content": f'[SYSTEM]You are a helpful assistant. You have access to the following functions: \n {str(functions_metadata)}\n\nTo use these functions respond with:\n<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", "arg_1": "value_1", ... }} }}  </functioncall>  [USER] {message} {vqa}'})
 
-    for qas in history:
-        messages.append({"role": "user", "content": f"{str(qas[0])}"})
-    
-    messages.append({"role": "user", "content": f'[SYSTEM]You are a helpful assistant with access to the following functions: \n {str(functions_metadata)}\n\nTo use these functions respond with:\n<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", "arg_1": "value_1", ... }} }}  </functioncall>  [USER] {message} {vqa}'})
-
-    response = client.chat_completion( messages, max_tokens=150)
+    # Call the LLM for response generation
+    response = client.chat_completion(messages, max_tokens=150)
     response = str(response)
     try:
         response = response[int(response.find("{")):int(response.index("</"))]
@@ -178,7 +97,8 @@ def respond(
     response = response.replace("\\'", "'")
     response = response.replace('\\"', '"')
     print(f"\n{response}")
-    # Extract JSON content from the response
+
+    # Process and return the response based on the function call
     try:
         json_data = json.loads(str(response))
         if json_data["name"] == "web_search":
@@ -192,7 +112,7 @@ def respond(
                 messages += f"\n<|im_start|>user\n{str(msg[0])}<|im_end|>"
                 messages += f"\n<|im_start|>assistant\n{str(msg[1])}<|im_end|>"
             messages+=f"\n<|im_start|>user\n{message_text} {vqa}<|im_end|>\n<|im_start|>web_result\n{web2}<|im_end|>\n<|im_start|>assistant\n"
-            stream = client_mixtral.text_generation(messages, **generate_kwargs)
+            stream = client_mixtral.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
             output = ""
             for response in stream:
                 if not response.token.text == "<|im_end|>":
@@ -212,7 +132,7 @@ def respond(
                 messages += f"\n<|start_header_id|>user\n{str(msg[0])}<|end_header_id|>"
                 messages += f"\n<|start_header_id|>assistant\n{str(msg[1])}<|end_header_id|>"
             messages+=f"\n<|start_header_id|>user\n{message_text} {vqa}<|end_header_id|>\n<|start_header_id|>assistant\n"
-            stream = client_llama.text_generation(messages, **generate_kwargs)
+            stream = client_llama.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
             output = ""
             for response in stream:
                 if not response.token.text == "<|eot_id|>":
@@ -224,7 +144,7 @@ def respond(
                 messages += f"\n<|start_header_id|>user\n{str(msg[0])}<|end_header_id|>"
                 messages += f"\n<|start_header_id|>assistant\n{str(msg[1])}<|end_header_id|>"
             messages+=f"\n<|start_header_id|>user\n{message_text} {vqa}<|end_header_id|>\n<|start_header_id|>assistant\n"
-            stream = client_llama.text_generation(messages, **generate_kwargs)
+            stream = client_llama.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
             output = ""
             for response in stream:
                 if not response.token.text == "<|eot_id|>":
@@ -236,24 +156,28 @@ def respond(
             messages += f"\n<|start_header_id|>user\n{str(msg[0])}<|end_header_id|>"
             messages += f"\n<|start_header_id|>assistant\n{str(msg[1])}<|end_header_id|>"
         messages+=f"\n<|start_header_id|>user\n{message_text} {vqa}<|end_header_id|>\n<|start_header_id|>assistant\n"
-        stream = client_llama.text_generation(messages, **generate_kwargs)
+        stream = client_llama.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
         output = ""
         for response in stream:
             if not response.token.text == "<|eot_id|>":
                 output += response.token.text
                 yield output
 
-demo = gr.ChatInterface(fn=respond,
-                        chatbot=gr.Chatbot(show_copy_button=True, likeable=True, layout="panel"), 
-                        title="OpenGPT 4o mini", 
-                        textbox=gr.MultimodalTextbox(), 
-                        multimodal=True,
-                        concurrency_limit=20,
-                        examples=[{"text": "Hy, who are you?",},
-                                  {"text": "What's the current price of Bitcoin",},
-                                  {"text": "Create A Beautiful image of Effiel Tower at Night",},
-                                  {"text": "Write me a Python function to calculate the first 10 digits of the fibonacci sequence.",},
-                                  {"text": "What's the colour of both of Car in given image","files": ["./car1.png", "./car2.png"]},],
-                        cache_examples=False)
-
+# Create the Gradio interface
+demo = gr.ChatInterface(
+    fn=respond,
+    chatbot=gr.Chatbot(show_copy_button=True, likeable=True, layout="panel"),
+    title="OpenGPT 4o mini",
+    textbox=gr.MultimodalTextbox(),
+    multimodal=True,
+    concurrency_limit=20,
+    examples=[
+        {"text": "Hy, who are you?",},
+        {"text": "What's the current price of Bitcoin",},
+        {"text": "Create A Beautiful image of Effiel Tower at Night",},
+        {"text": "Write me a Python function to calculate the first 10 digits of the fibonacci sequence.",},
+        {"text": "What's the colour of both of Car in given image", "files": ["./car1.png", "./car2.png"]},
+    ],
+    cache_examples=False,
+)
 demo.launch()