Default Agent

Dockerfile

@@ -1,12 +1,15 @@
 # Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
 # you will also find guides on how best to write your Dockerfile
 
-FROM python:3.10
+FROM python:3.12.3
 
 RUN useradd -m -u 1000 user
 USER user
 ENV PATH="/home/user/.local/bin:$PATH"
 
+# Add environment variable to force CPU usage
+ENV LLAMA_CPP_FORCE_CPU=1
+
 # Create a directory for the app
 WORKDIR /app
 

interaction_history.log

@@ -14,4 +14,32 @@ You are an AI model expert in computational biology performing autonomous real r
 <think>
 Okay, so I'm trying to figure out how to discover something new in the field of nerve regeneration. I'm not very familiar with this area, but I know it's related to stem cell research. Let me break down what I know and try to understand the problem.
 
-First, I know that nerve regeneration
+First, I know that nerve regenerationSystem prompt:
+
+You are an AI model expert in computational biology performing autonomous real research.
+        Your goal is to discover something new and relevant in the field of nerve regeneration.
+        You can execute shell commands in the Docker container the app is being executed.
+        With access to the following tools:
+
+- **shell**:
+   - Description: Execute shell commands.
+   - Input: {"command": "ls -l"}
+   - Output: {"stdout": "...", "stderr": "..."}
+
+🚀 Autonomous computational biology research initiated!
+<think>
+Okay, so I'm trying to figure out how to discover something new in the field of nerve regeneration.System prompt:
+
+You are an AI model expert in computational biology performing autonomous real research.
+        Your goal is to discover something new and relevant in the field of nerve regeneration.
+        You can execute shell commands in the Docker container the app is being executed.
+        With access to the following tools:
+
+- **shell**:
+   - Description: Execute shell commands.
+   - Input: {"command": "ls -l"}
+   - Output: {"stdout": "...", "stderr": "..."}
+
+🚀 Autonomous computational biology research initiated!
+<think>
+Okay, so I'm trying to figure out how to discover something new in the field of nerve regeneration. I'm not very familiar with this area, but I know it's related to

main.py

@@ -197,19 +197,16 @@ async def async_generator_from_sync(sync_gen_func, *args, **kwargs):
 # Background response generator without requiring a WebSocket.
 #
 async def generate_response_background(conversation):
-    """Generate a model response asynchronously."""
-    #template = Template(CHAT_TEMPLATE)
-    #prompt = template.render(messages=conversation)
-    #logging.info(f"Prompt: {prompt}")
+    logging.info(f"Starting generation with conversation: {conversation}")
     async for token_chunk in async_generator_from_sync(
-        llm.create_chat_completion, 
-        messages=conversation, 
+        llm.create_chat_completion,
+        messages=conversation,
         stream=True,
         tools=tools_list,
         max_tokens=2048
     ):
-        # Extract token from OpenAI-compatible format
-        yield token_chunk  # Yield the token string directly
+        logging.debug(f"Raw token chunk: {json.dumps(token_chunk, indent=2)}")
+        yield token_chunk
         await asyncio.sleep(0)
 
 #
@@ -225,18 +222,28 @@ async def run_research_forever():
     while True:
         full_response = ""
         try:
-            # Generate the model response and accumulate the full text.
-            logging.info["Before generate_response_background"]
             async for token in generate_response_background(conversation):
-                logging.info (f"generate_response_background yields: {token}")
-                token_text = token["choices"][0]["delta"].get("content", "")
+                # Safely extract delta
+                delta = token["choices"][0].get("delta", {})
+                
+                # Handle text content
+                token_text = delta.get("content", "")  # Default to empty string
                 full_response += token_text
-                # Log each token individually
+                
+                # Handle tool calls (critical for container environment)
+                if "tool_calls" in delta:
+                    tool_calls = delta["tool_calls"]
+                    # Process tool call deltas here (append to full_response or log)
+                    tool_call_str = json.dumps(tool_calls)
+                    full_response += f"\n🔧 Tool Call: {tool_call_str}\n"
+                
+                # Logging remains the same
                 with open(log_path, "a") as f:
                     f.write(token_text)
                     f.flush()
-                # Optionally, check if a finish reason is provided
-                if token['choices'][0].get("finish_reason", "") is not None:
+                
+                # Check for finish reason
+                if token['choices'][0].get("finish_reason"):
                 
                     # The presence of a finish reason (like "stop") indicates that generation is complete.
                     # Append the assistant's response to the conversation log.

req.txt

@@ -0,0 +1,49 @@
+annotated-types==0.7.0
+anyio==4.8.0
+black==25.1.0
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+colorama==0.4.6
+cssbeautifier==1.15.2
+diskcache==5.6.3
+djlint==1.36.4
+EditorConfig==0.17.0
+fastapi==0.115.8
+filelock==3.17.0
+fsspec==2025.2.0
+h11==0.14.0
+httptools==0.6.4
+huggingface-hub==0.28.1
+idna==3.10
+Jinja2==3.1.5
+jinja2-cli==0.8.2
+jsbeautifier==1.15.2
+json5==0.10.0
+llama_cpp_python==0.3.7
+MarkupSafe==3.0.2
+mypy-extensions==1.0.0
+numpy==2.2.2
+packaging==24.2
+pathspec==0.12.1
+platformdirs==4.3.6
+prettier==0.0.7
+pydantic==2.10.6
+pydantic-settings==2.7.1
+pydantic_core==2.27.2
+python-dotenv==1.0.1
+PyYAML==6.0.2
+regex==2024.11.6
+requests==2.32.3
+six==1.17.0
+sniffio==1.3.1
+sse-starlette==2.2.1
+starlette==0.45.3
+starlette-context==0.3.6
+tqdm==4.67.1
+typing_extensions==4.12.2
+urllib3==2.3.0
+uvicorn==0.34.0
+uvloop==0.21.0
+watchfiles==1.0.4
+websockets==14.2

requirements.txt

@@ -1,4 +1,4 @@
-jinja2
-fastapi
-uvicorn[standard]
-llama.cpp.python[server]
+jinja2==3.1.5
+fastapi==0.115.8
+uvicorn[standard]==0.34.0
+llama.cpp.python[server]==0.3.7