fix unable to answer agent

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+B5T0Y3HsLuJvW5iB4YlcNHlsdu87kGJ55tukmi8mxdAQ4Q7e2RCOFvu396j3x+UC
+B5iPNgiV5+I3lg02dZ77DnKxHZu8A/lJBdiB3QW0KtZB6awBdpUKD9jf1b0SHzUv
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+rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV
+HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
+hkiG9w0BAQsFAAOCAgEAVR9YqbyyqFDQDLHYGmkgJykIrGF1XIpu+ILlaS/V9lZL
+ubhzEFnTIZd+50xx+7LSYK05qAvqFyFWhfFQDlnrzuBZ6brJFe+GnY+EgPbk6ZGQ
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+ORAzI4JMPJ+GslWYHb4phowim57iaztXOoJwTdwJx4nLCgdNbOhdjsnvzqvHu7Ur
+TkXWStAmzOVyyghqpZXjFaH3pO3JLF+l+/+sKAIuvtd7u+Nxe5AW0wdeRlN8NwdC
+jNPElpzVmbUq4JUagEiuTDkHzsxHpFKVK7q4+63SM1N95R1NbdWhscdCb+ZAJzVc
+oyi3B43njTOQ5yOf+1CceWxG1bQVs5ZufpsMljq4Ui0/1lvh+wjChP4kqKOJ2qxq
+4RgqsahDYVvTH9w7jXbyLeiNdd8XM2w9U/t7y0Ff/9yi0GE44Za4rF2LN9d11TPA
+mRGunUHBcnWEvgJBQl9nJEiU0Zsnvgc/ubhPgXRR4Xq37Z0j4r7g1SgEEzwxA57d
+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

Gradio_UI.py

@@ -1,295 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import mimetypes
-import os
-import re
-import shutil
-from typing import Optional
-
-from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types
-from smolagents.agents import ActionStep, MultiStepAgent
-from smolagents.memory import MemoryStep
-from smolagents.utils import _is_package_available
-
-
-def pull_messages_from_step(
-    step_log: MemoryStep,
-):
-    """Extract ChatMessage objects from agent steps with proper nesting"""
-    import gradio as gr
-
-    if isinstance(step_log, ActionStep):
-        # Output the step number
-        step_number = f"Step {step_log.step_number}" if step_log.step_number is not None else ""
-        yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")
-
-        # First yield the thought/reasoning from the LLM
-        if hasattr(step_log, "model_output") and step_log.model_output is not None:
-            # Clean up the LLM output
-            model_output = step_log.model_output.strip()
-            # Remove any trailing <end_code> and extra backticks, handling multiple possible formats
-            model_output = re.sub(r"```\s*<end_code>", "```", model_output)  # handles ```<end_code>
-            model_output = re.sub(r"<end_code>\s*```", "```", model_output)  # handles <end_code>```
-            model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output)  # handles ```\n<end_code>
-            model_output = model_output.strip()
-            yield gr.ChatMessage(role="assistant", content=model_output)
-
-        # For tool calls, create a parent message
-        if hasattr(step_log, "tool_calls") and step_log.tool_calls is not None:
-            first_tool_call = step_log.tool_calls[0]
-            used_code = first_tool_call.name == "python_interpreter"
-            parent_id = f"call_{len(step_log.tool_calls)}"
-
-            # Tool call becomes the parent message with timing info
-            # First we will handle arguments based on type
-            args = first_tool_call.arguments
-            if isinstance(args, dict):
-                content = str(args.get("answer", str(args)))
-            else:
-                content = str(args).strip()
-
-            if used_code:
-                # Clean up the content by removing any end code tags
-                content = re.sub(r"```.*?\n", "", content)  # Remove existing code blocks
-                content = re.sub(r"\s*<end_code>\s*", "", content)  # Remove end_code tags
-                content = content.strip()
-                if not content.startswith("```python"):
-                    content = f"```python\n{content}\n```"
-
-            parent_message_tool = gr.ChatMessage(
-                role="assistant",
-                content=content,
-                metadata={
-                    "title": f"🛠️ Used tool {first_tool_call.name}",
-                    "id": parent_id,
-                    "status": "pending",
-                },
-            )
-            yield parent_message_tool
-
-            # Nesting execution logs under the tool call if they exist
-            if hasattr(step_log, "observations") and (
-                step_log.observations is not None and step_log.observations.strip()
-            ):  # Only yield execution logs if there's actual content
-                log_content = step_log.observations.strip()
-                if log_content:
-                    log_content = re.sub(r"^Execution logs:\s*", "", log_content)
-                    yield gr.ChatMessage(
-                        role="assistant",
-                        content=f"{log_content}",
-                        metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},
-                    )
-
-            # Nesting any errors under the tool call
-            if hasattr(step_log, "error") and step_log.error is not None:
-                yield gr.ChatMessage(
-                    role="assistant",
-                    content=str(step_log.error),
-                    metadata={"title": "💥 Error", "parent_id": parent_id, "status": "done"},
-                )
-
-            # Update parent message metadata to done status without yielding a new message
-            parent_message_tool.metadata["status"] = "done"
-
-        # Handle standalone errors but not from tool calls
-        elif hasattr(step_log, "error") and step_log.error is not None:
-            yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})
-
-        # Calculate duration and token information
-        step_footnote = f"{step_number}"
-        if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):
-            token_str = (
-                f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
-            )
-            step_footnote += token_str
-        if hasattr(step_log, "duration"):
-            step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None
-            step_footnote += step_duration
-        step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """
-        yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")
-        yield gr.ChatMessage(role="assistant", content="-----")
-
-
-def stream_to_gradio(
-    agent,
-    task: str,
-    reset_agent_memory: bool = False,
-    additional_args: Optional[dict] = None,
-):
-    """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
-    if not _is_package_available("gradio"):
-        raise ModuleNotFoundError(
-            "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
-        )
-    import gradio as gr
-
-    total_input_tokens = 0
-    total_output_tokens = 0
-
-    for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):
-        # Track tokens if model provides them
-        if hasattr(agent.model, "last_input_token_count"):
-            total_input_tokens += agent.model.last_input_token_count
-            total_output_tokens += agent.model.last_output_token_count
-            if isinstance(step_log, ActionStep):
-                step_log.input_token_count = agent.model.last_input_token_count
-                step_log.output_token_count = agent.model.last_output_token_count
-
-        for message in pull_messages_from_step(
-            step_log,
-        ):
-            yield message
-
-    final_answer = step_log  # Last log is the run's final_answer
-    final_answer = handle_agent_output_types(final_answer)
-
-    if isinstance(final_answer, AgentText):
-        yield gr.ChatMessage(
-            role="assistant",
-            content=f"**Final answer:**\n{final_answer.to_string()}\n",
-        )
-    elif isinstance(final_answer, AgentImage):
-        yield gr.ChatMessage(
-            role="assistant",
-            content={"path": final_answer.to_string(), "mime_type": "image/png"},
-        )
-    elif isinstance(final_answer, AgentAudio):
-        yield gr.ChatMessage(
-            role="assistant",
-            content={"path": final_answer.to_string(), "mime_type": "audio/wav"},
-        )
-    else:
-        yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")
-
-
-class GradioUI:
-    """A one-line interface to launch your agent in Gradio"""
-
-    def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None):
-        if not _is_package_available("gradio"):
-            raise ModuleNotFoundError(
-                "Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
-            )
-        self.agent = agent
-        self.file_upload_folder = file_upload_folder
-        if self.file_upload_folder is not None:
-            if not os.path.exists(file_upload_folder):
-                os.mkdir(file_upload_folder)
-
-    def interact_with_agent(self, prompt, messages):
-        import gradio as gr
-
-        messages.append(gr.ChatMessage(role="user", content=prompt))
-        yield messages
-        for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):
-            messages.append(msg)
-            yield messages
-        yield messages
-
-    def upload_file(
-        self,
-        file,
-        file_uploads_log,
-        allowed_file_types=[
-            "application/pdf",
-            "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-            "text/plain",
-        ],
-    ):
-        """
-        Handle file uploads, default allowed types are .pdf, .docx, and .txt
-        """
-        import gradio as gr
-
-        if file is None:
-            return gr.Textbox("No file uploaded", visible=True), file_uploads_log
-
-        try:
-            mime_type, _ = mimetypes.guess_type(file.name)
-        except Exception as e:
-            return gr.Textbox(f"Error: {e}", visible=True), file_uploads_log
-
-        if mime_type not in allowed_file_types:
-            return gr.Textbox("File type disallowed", visible=True), file_uploads_log
-
-        # Sanitize file name
-        original_name = os.path.basename(file.name)
-        sanitized_name = re.sub(
-            r"[^\w\-.]", "_", original_name
-        )  # Replace any non-alphanumeric, non-dash, or non-dot characters with underscores
-
-        type_to_ext = {}
-        for ext, t in mimetypes.types_map.items():
-            if t not in type_to_ext:
-                type_to_ext[t] = ext
-
-        # Ensure the extension correlates to the mime type
-        sanitized_name = sanitized_name.split(".")[:-1]
-        sanitized_name.append("" + type_to_ext[mime_type])
-        sanitized_name = "".join(sanitized_name)
-
-        # Save the uploaded file to the specified folder
-        file_path = os.path.join(self.file_upload_folder, os.path.basename(sanitized_name))
-        shutil.copy(file.name, file_path)
-
-        return gr.Textbox(f"File uploaded: {file_path}", visible=True), file_uploads_log + [file_path]
-
-    def log_user_message(self, text_input, file_uploads_log):
-        return (
-            text_input
-            + (
-                f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"
-                if len(file_uploads_log) > 0
-                else ""
-            ),
-            "",
-        )
-
-    def launch(self, **kwargs):
-        import gradio as gr
-
-        with gr.Blocks(fill_height=True) as demo:
-            stored_messages = gr.State([])
-            file_uploads_log = gr.State([])
-            chatbot = gr.Chatbot(
-                label="Agent",
-                type="messages",
-                avatar_images=(
-                    None,
-                    "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
-                ),
-                scale=1,
-            )
-            # If an upload folder is provided, enable the upload feature
-            if self.file_upload_folder is not None:
-                upload_file = gr.File(label="Upload a file")
-                upload_status = gr.Textbox(label="Upload Status", interactive=False, visible=False)
-                upload_file.change(
-                    self.upload_file,
-                    [upload_file, file_uploads_log],
-                    [upload_status, file_uploads_log],
-                )
-            text_input = gr.Textbox(lines=1, label="Chat Message")
-            text_input.submit(
-                self.log_user_message,
-                [text_input, file_uploads_log],
-                [stored_messages, text_input],
-            ).then(self.interact_with_agent, [stored_messages, chatbot], [chatbot])
-
-        demo.launch(debug=True, share=True, **kwargs)
-
-
-__all__ = ["stream_to_gradio", "GradioUI"]

agent.py

@@ -742,18 +742,7 @@ def build_graph(provider: str = "groq"):
 
     def retriever(state: MessagesState):
         """Retriever node to find similar questions."""
-        if vector_store is not None:
-            try:
-                similar_question = vector_store.similarity_search(state["messages"][0].content)
-
-                if similar_question:
-                    example_msg = HumanMessage(
-                        content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
-                    )
-                    return {"messages": [sys_msg] + state["messages"] + [example_msg]}
-            except Exception as e:
-                print(f"Error in retriever: {e}")
-        
+        # Retriever disabled to avoid Supabase connection issues
         return {"messages": [sys_msg] + state["messages"]}
 
     builder = StateGraph(MessagesState)

app.js

@@ -67,6 +67,22 @@ const App = () => {
     if (window.innerWidth < 1024) setSidebarOpen(false);
   };
 
+  const handleDeleteSession = (e, sessionId) => {
+    e.stopPropagation();
+    setSessions(prev => {
+      const filtered = prev.filter(s => s.id !== sessionId);
+      if (filtered.length === 0) {
+        const newSession = createNewSession();
+        setCurrentSessionId(newSession.id);
+        return [newSession];
+      }
+      if (sessionId === currentSessionId) {
+        setCurrentSessionId(filtered[0].id);
+      }
+      return filtered;
+    });
+  };
+
   const updateSessionMessages = (sessionId, newMessage) => {
     setSessions(prev => prev.map(s => 
       s.id === sessionId 
@@ -216,6 +232,15 @@ const App = () => {
               >
                 <Icon path={ICONS.chat} className="w-4 h-4 flex-shrink-0" />
                 <span className="flex-1 text-xs truncate">{session.title}</span>
+                <button
+                  onClick={(e) => handleDeleteSession(e, session.id)}
+                  className="opacity-0 group-hover:opacity-100 p-1 hover:bg-red-500/20 rounded transition-all"
+                  title="Delete chat"
+                >
+                  <svg className="w-3.5 h-3.5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                    <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M19 7l-.867 12.142A2 2 0 0116.138 21H7.862a2 2 0 01-1.995-1.858L5 7m5 4v6m4-6v6m1-10V4a1 1 0 00-1-1h-4a1 1 0 00-1 1v3M4 7h16" />
+                  </svg>
+                </button>
               </div>
             ))
           )}

app.py

@@ -1,69 +1,116 @@
-from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
-import datetime
-import requests
-import pytz
-import yaml
-from tools.final_answer import FinalAnswerTool
+import os
+import uvicorn
+import json
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List, Optional
 
-from Gradio_UI import GradioUI
+try:
+    from logic import GaiaApp
+except ImportError:
+    class GaiaApp:
+        def process_input(self, msg, hist, files):
+            return "Error: logic.py not found", hist, None
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
-@tool
-def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
-    #Keep this format for the description / args / args description but feel free to modify the tool
-    """A tool that does nothing yet 
-    Args:
-        arg1: the first argument
-        arg2: the second argument
-    """
-    return "What magic will you build ?"
+app = FastAPI()
 
-@tool
-def get_current_time_in_timezone(timezone: str) -> str:
-    """A tool that fetches the current local time in a specified timezone.
-    Args:
-        timezone: A string representing a valid timezone (e.g., 'America/New_York').
-    """
-    try:
-        # Create timezone object
-        tz = pytz.timezone(timezone)
-        # Get current time in that timezone
-        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
-        return f"The current local time in {timezone} is: {local_time}"
-    except Exception as e:
-        return f"Error fetching time for timezone '{timezone}': {str(e)}"
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
+gaia_engine = GaiaApp()
 
-final_answer = FinalAnswerTool()
+class Message(BaseModel):
+    role: str
+    content: str
 
-# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
+class ChatRequest(BaseModel):
+    message: str
+    history: List[Message]
 
-model = HfApiModel(
-max_tokens=2096,
-temperature=0.5,
-model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-custom_role_conversions=None,
-)
+def format_history(messages: List[Message]) -> List[tuple]:
+    """Convert message history from React format to agent format."""
+    formatted = []
+    for msg in messages:
+        if msg.role == 'user':
+            formatted.append((msg.content, None))
+        elif msg.role == 'model' and formatted and formatted[-1][1] is None:
+            formatted[-1] = (formatted[-1][0], msg.content)
+    return formatted
 
+@app.post("/api/chat")
+async def chat_endpoint(
+    message: str = Form(...),
+    history: str = Form("[]"),
+    files: List[UploadFile] = File(default=[])
+):
+    """Process chat message with optional file uploads and return agent response."""
+    try:
+        messages = json.loads(history)
+        formatted_history = format_history([Message(**msg) for msg in messages])
+        
+        file_paths = []
+        if files:
+            os.makedirs("uploads", exist_ok=True)
+            for file in files:
+                file_path = f"uploads/{file.filename}"
+                with open(file_path, "wb") as f:
+                    content = await file.read()
+                    f.write(content)
+                file_paths.append(file_path)
+        
+        generator = gaia_engine.process_input(message, formatted_history, file_paths or None)
+        
+        final_state = None
+        for step in generator:
+            final_state = step
+            
+        if final_state and final_state[1]:
+            # final_state[1] is the history as list of dicts with role/content
+            last_message = final_state[1][-1]
+            if isinstance(last_message, dict):
+                bot_response = last_message.get("content", "No response")
+            else:
+                # Fallback for tuple format
+                bot_response = last_message[1] if len(last_message) > 1 else "No response"
+            return {"reply": bot_response}
+        
+        return {"reply": "No response from agent."}
+    except Exception as e:
+        print(f"Chat endpoint error: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return {"reply": f"Error: {str(e)}"}
 
-# Import tool from Hub
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+ALLOWED_EXTENSIONS = {".css", ".js", ".png", ".html"}
 
-with open("prompts.yaml", 'r') as stream:
-    prompt_templates = yaml.safe_load(stream)
-    
-agent = CodeAgent(
-    model=model,
-    tools=[final_answer], ## add your tools here (don't remove final answer)
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
-    name=None,
-    description=None,
-    prompt_templates=prompt_templates
-)
+@app.get("/")
+async def serve_index():
+    return FileResponse("index.html")
 
+@app.get("/{filename}")
+async def serve_static(filename: str):
+    ext = os.path.splitext(filename)[1]
+    if ext in ALLOWED_EXTENSIONS and os.path.exists(filename):
+        return FileResponse(filename)
+    return FileResponse("index.html", status_code=404)
 
-GradioUI(agent).launch()
+if __name__ == "__main__":
+    print("\n" + "="*60)
+    print("🚀 GAIA Agent Starting...")
+    print("="*60)
+    print(f"📍 Open in browser: http://localhost:7860")
+    print(f"📍 Or use: http://127.0.0.1:7860")
+    print("="*60 + "\n")
+    uvicorn.run(
+        "app:app",
+        host="0.0.0.0",
+        port=7860,
+        reload=True
+    )

server.py

@@ -1,97 +0,0 @@
-import os
-import uvicorn
-import json
-from fastapi import FastAPI, UploadFile, File, Form
-from fastapi.responses import FileResponse
-from fastapi.staticfiles import StaticFiles
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-from typing import List, Optional
-
-try:
-    from logic import GaiaApp
-except ImportError:
-    class GaiaApp:
-        def process_input(self, msg, hist, files):
-            return "Error: logic.py not found", hist, None
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-gaia_engine = GaiaApp()
-
-class Message(BaseModel):
-    role: str
-    content: str
-
-class ChatRequest(BaseModel):
-    message: str
-    history: List[Message]
-
-def format_history(messages: List[Message]) -> List[tuple]:
-    """Convert message history from React format to agent format."""
-    formatted = []
-    for msg in messages:
-        if msg.role == 'user':
-            formatted.append((msg.content, None))
-        elif msg.role == 'model' and formatted and formatted[-1][1] is None:
-            formatted[-1] = (formatted[-1][0], msg.content)
-    return formatted
-
-@app.post("/api/chat")
-async def chat_endpoint(
-    message: str = Form(...),
-    history: str = Form("[]"),
-    files: List[UploadFile] = File(default=[])
-):
-    """Process chat message with optional file uploads and return agent response."""
-    try:
-        messages = json.loads(history)
-        formatted_history = format_history([Message(**msg) for msg in messages])
-        
-        file_paths = []
-        if files:
-            os.makedirs("uploads", exist_ok=True)
-            for file in files:
-                file_path = f"uploads/{file.filename}"
-                with open(file_path, "wb") as f:
-                    content = await file.read()
-                    f.write(content)
-                file_paths.append(file_path)
-        
-        generator = gaia_engine.process_input(message, formatted_history, file_paths or None)
-        
-        final_state = None
-        for step in generator:
-            final_state = step
-            
-        if final_state and final_state[1]:
-            bot_response = final_state[1][-1][1]
-            return {"reply": bot_response}
-        
-        return {"reply": "No response from agent."}
-    except Exception as e:
-        print(f"Error: {e}")
-        return {"reply": f"Error: {str(e)}"}
-
-ALLOWED_EXTENSIONS = {".css", ".js", ".png", ".html"}
-
-@app.get("/")
-async def serve_index():
-    return FileResponse("index.html")
-
-@app.get("/{filename}")
-async def serve_static(filename: str):
-    ext = os.path.splitext(filename)[1]
-    if ext in ALLOWED_EXTENSIONS and os.path.exists(filename):
-        return FileResponse(filename)
-    return FileResponse("index.html", status_code=404)
-
-if __name__ == "__main__":
-    uvicorn.run("server:app", host="0.0.0.0", port=7860, reload=True)