streamlit app

app.py

@@ -0,0 +1,164 @@
+import streamlit as st
+from huggingface_hub import InferenceClient
+import os
+from typing import Iterator
+
+
+API_KEY = os.getenv("TOGETHER_API_KEY")
+if not API_KEY:
+    raise ValueError("API key is missing! Make sure TOGETHER_API_KEY is set in the Secrets.")
+
+
+# Initialize the client with Together AI provider
+@st.cache_resource
+def get_client():
+    return InferenceClient(
+        provider="together",
+         api_key=API_KEY
+    )
+
+def process_file(file) -> str:
+    """Process uploaded file and return its content"""
+    if file is None:
+        return ""
+    
+    try:
+        content = file.getvalue().decode('utf-8')
+        return content
+    except Exception as e:
+        return f"Error reading file: {str(e)}"
+
+def generate_response(
+    message: str,
+    history: list[tuple[str, str]],
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+    file=None
+) -> Iterator[str]:
+    """Generate streaming response from the model"""
+    client = get_client()
+    
+    # Process file if uploaded
+    file_content = process_file(file) if file else ""
+    
+    # If there's file content, append it to the message
+    if file_content:
+        message = f"File content:\n{file_content}\n\nUser message:\n{message}"
+
+    messages = [{"role": "system", "content": system_message}]
+
+    # Add conversation history
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
+
+    # Add current message
+    messages.append({"role": "user", "content": message})
+
+    try:
+        stream = client.chat.completions.create(
+            model="deepseek-ai/DeepSeek-R1",
+            messages=messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        )
+        
+        for chunk in stream:
+            if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
+                yield chunk.choices[0].delta.content
+                
+    except Exception as e:
+        yield f"Error: {str(e)}"
+
+def main():
+    st.set_page_config(page_title="DeepSeek Chat", page_icon="💭", layout="wide")
+    
+    # Initialize session state for chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    st.title("DeepSeek Chat with File Upload")
+    st.markdown("Chat with DeepSeek AI model. You can optionally upload files for the model to analyze.")
+
+    # Sidebar for parameters
+    with st.sidebar:
+        st.header("Settings")
+        system_message = st.text_area(
+            "System Message",
+            value="You are a friendly Chatbot.",
+            height=100
+        )
+        max_tokens = st.slider(
+            "Max Tokens",
+            min_value=1,
+            max_value=8192,
+            value=8192,
+            step=1
+        )
+        temperature = st.slider(
+            "Temperature",
+            min_value=0.1,
+            max_value=4.0,
+            value=0.0,
+            step=0.1
+        )
+        top_p = st.slider(
+            "Top-p (nucleus sampling)",
+            min_value=0.1,
+            max_value=1.0,
+            value=0.95,
+            step=0.05
+        )
+        uploaded_file = st.file_uploader(
+            "Upload File (optional)",
+            type=['txt', 'py', 'md', 'csv']
+        )
+
+    # Display chat messages
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+
+    # Chat input
+    if prompt := st.chat_input("What would you like to know?"):
+        # Display user message
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.write(prompt)
+
+        # Generate and display assistant response
+        with st.chat_message("assistant"):
+            response_placeholder = st.empty()
+            full_response = ""
+            
+            # Get message history for context
+            history = [(msg["content"], next_msg["content"]) 
+                      for msg, next_msg in zip(st.session_state.messages[::2], st.session_state.messages[1::2])]
+            
+            # Stream the response
+            for response_chunk in generate_response(
+                prompt,
+                history,
+                system_message,
+                max_tokens,
+                temperature,
+                top_p,
+                uploaded_file
+            ):
+                full_response += response_chunk
+                print(full_response)
+                response_placeholder.markdown(full_response + "▌")
+            
+            response_placeholder.markdown(full_response)
+        
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+huggingface_hub==0.25.2
+streamlit