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Dockerfile

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+# Use a smaller base image
+FROM python:3.9-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    make \
+    libffi-dev \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy only requirements first for caching
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the app files
+COPY app.py .
+
+# Expose the port that Streamlit runs on
+EXPOSE 8501
+
+# Run the app
+CMD ["streamlit", "run", "app.py"]

app.py

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+import streamlit as st
+import google.generativeai as genai
+from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import PromptTemplate
+import os
+
+# Streamlit page configuration
+st.set_page_config(page_title="AI Tool", page_icon=":robot:")
+st.title("GPT Clone")
+st.sidebar.title("Select your LLM Model")
+
+# Sidebar to select the model
+model = st.sidebar.selectbox("Please select any model:", 
+                             ("Gemini", "Mistral", "Llama"), 
+                             placeholder="Select your LLM model...")
+
+st.write("Your LLM Model is:", model)
+
+# Initialize API key state
+if "api_key" not in st.session_state:
+    st.session_state["api_key"] = ''
+
+# Function to get API key input from the user
+def get_api_key():
+    if model == "Gemini":
+        st.session_state["api_key"] = st.sidebar.text_input("Please enter your Gemini API key", type='password')
+    else:
+        st.session_state["api_key"] = st.sidebar.text_input("Please enter your HuggingFace API key", type='password')
+    return st.session_state["api_key"]
+
+# Function to interact with HuggingFace models
+def invoke_hugging_llm(model_name, api_key, prompt):
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
+    llm = HuggingFaceEndpoint(repo_id=model_name)
+    response = llm.invoke(prompt)
+    return response
+
+import logging
+logging.basicConfig(level=logging.DEBUG)
+
+def get_llm_response(api_key, prompt):
+    logging.debug(f"Using API key: {api_key}")
+    logging.debug(f"Prompt: {prompt}")
+    try:
+        if model == "Mistral":
+            model_name = "mistralai/Mistral-7B-Instruct-v0.3"
+            response = invoke_hugging_llm(model_name, api_key, prompt)
+        elif model == "Llama":
+            model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
+            response = invoke_hugging_llm(model_name, api_key, prompt)
+        elif model == "Gemini":
+            os.environ['GOOGLE_API_KEY'] = api_key
+            genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
+            llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+            response = llm.invoke(prompt)
+            logging.debug(f"Gemini response: {response}")
+            return response.content
+    except Exception as e:
+        logging.error(f"Error invoking model: {e}")
+        raise e
+    return response
+
+# Get API key
+api_key = get_api_key()
+
+# Display success message if API key is provided
+if api_key:
+    st.success("API Key Acquired")
+    
+    # Text input for user's question
+    question = st.text_input("Ask your question")
+    
+    # Button to submit the question
+    button2 = st.button("Submit")
+
+    # Search tool integration (DuckDuckGo)
+    from phi.assistant import Assistant
+    from phi.tools.duckduckgo import DuckDuckGo
+
+    # Initialize the search tool
+    search_tool = Assistant(tools=[DuckDuckGo()], show_tool_calls=True)
+
+    # Check if a question has been entered
+    search_result = None
+    if question:
+        try:
+            search_result = search_tool.run(question)  # Adjusted from 'print_response' to 'run'
+        except Exception as e:
+            st.error(f"Error performing search: {str(e)}")
+            search_result = "No search results found."
+
+    # Create the prompt using the search result
+    template = """You are an AI assistant. Provide relevant answers to the user's question. 
+                The user's question is: {question}. 
+                If the user asks about current affairs, use the DuckDuckGo search result as context. 
+                The DuckDuckGo search result is: {search}"""
+
+    example_prompt = PromptTemplate(input_variables=["question", "search"], template=template)
+    prompt = example_prompt.format(question=question, search=search_result)
+
+    # If the submit button is clicked, get the response from the selected model
+    if button2:
+        if search_result:
+            response = get_llm_response(st.session_state["api_key"], prompt)
+            st.write(response)
+        else:
+            st.warning("Please enter a valid question to search.")

requirements.txt

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+langchain_community==0.3.0
+langchain_core==0.3.5
+langchain_google_genai==2.0.0
+phi==0.6.7
+phidata==2.4.20
+protobuf>=3.20,<6
+streamlit==1.37.0
+duckduckgo_search==6.2.11b1
+huggingface_hub==0.25.1