Allen assignment

Exercises/Exercise_1/app.py

@@ -0,0 +1,22 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+
+
+app = FastAPI(title="Hello LLM API")
+
+# Load Hugging Face text generation pipeline
+generator = pipeline("text-generation", model="distilgpt2")
+
+
+class Prompt(BaseModel):
+    text: str
+    max_length: int = 50
+
+@app.post("/hello-llm")
+async def hello_llm(prompt: Prompt):
+    """
+    Generate text from a given prompt using distilgpt2.
+    """
+    output = generator(prompt.text, max_length=prompt.max_length, num_return_sequences=1)
+    return {"prompt": prompt.text, "generated_text": output[0]["generated_text"]}

Exercises/Exercise_1/requirements.txt

@@ -0,0 +1,15 @@
+langchain_chroma
+fastapi
+uvicorn
+pydantic
+python-multipart 
+transformers
+torch
+langchain
+langchain_community  
+diffusers
+accelerate
+safetensors
+Pillow
+python_dotenv
+streamlit

Exercises/Exercise_1/streamlit.py

@@ -0,0 +1,67 @@
+# streamlit_app.py
+import streamlit as st
+import requests
+
+st.set_page_config(layout="wide", page_title="Hello LLM", page_icon="🤖")
+
+# Sidebar
+with st.sidebar:
+    st.title("🤖 Hello LLM")
+    st.markdown("*play with text generation*")
+    st.markdown("___")
+
+    st.subheader("📊 Session Stats")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.header("Messages")
+        if "messages" in st.session_state:
+            st.markdown(f"# {len(st.session_state.messages) // 2}")
+        else:
+            st.markdown("# 0")
+    with col2:
+        st.header("Total")
+        if "messages" in st.session_state:
+            st.markdown(f"# {len(st.session_state.messages)}")
+        else:
+            st.markdown("# 0")
+    st.markdown("___")
+
+    st.header("⚙ Controls")
+    max_length = st.slider("Max Length", min_value=10, max_value=200, value=50, step=5)
+    st.markdown("___")
+
+# Main Chat Section
+st.subheader("🗯 Chat with Hello LLM")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+chat_box = st.container(height=500, border=True)
+
+# Display chat history
+with chat_box:
+    for i, msg in enumerate(st.session_state.messages):
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["text"])
+
+# Input area
+user_input = st.chat_input("Type your prompt here...")
+if user_input:
+    # Save user message
+    st.session_state.messages.append({"role": "user", "text": user_input})
+
+    # Send to FastAPI
+    with st.spinner("AI is generating response..."):
+        try:
+            url = "http://127.0.0.1:8000/hello-llm"  # FastAPI endpoint
+            payload = {"text": user_input, "max_length": max_length}
+            response = requests.post(url, json=payload)
+            response.raise_for_status()
+            data = response.json()
+            ai_reply = data["generated_text"]
+        except requests.exceptions.RequestException as e:
+            ai_reply = f"⚠️ Error: {e}"
+
+    # Save assistant reply
+    st.session_state.messages.append({"role": "assistant", "text": ai_reply})
+    st.rerun()

Exercises/Exercise_2/app.py

@@ -0,0 +1,42 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+
+# Initialize FastAPI app
+app = FastAPI(title="Summarizer API")
+
+# Load Hugging Face summarization pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Request body schema
+class SummarizeRequest(BaseModel):
+    text: str
+    max_length: int = 150
+    min_length: int = 30
+    temperature: float = 1.0  
+    top_k: int = 50            
+    top_p: float = 0.9         
+
+
+@app.post("/summarize")
+async def summarize(request: SummarizeRequest):
+    """
+    Summarize a long text using facebook/bart-large-cnn
+    with controllable generation parameters.
+    """
+    try:
+        summary = summarizer(
+            request.text,
+            max_length=request.max_length,
+            min_length=request.min_length,
+            do_sample=True,                  
+            temperature=request.temperature, 
+            top_k=request.top_k,             
+            top_p=request.top_p,             
+        )
+        return {
+            "input_text": request.text,
+            "summary_text": summary[0]["summary_text"]
+        }
+    except Exception as e:
+        return {"error": str(e)}

Exercises/Exercise_2/frontend.py

@@ -0,0 +1,77 @@
+# streamlit_app.py
+import streamlit as st
+import requests
+
+st.set_page_config(layout="wide", page_title="Summarizer LLM", page_icon="📝")
+
+# Sidebar
+with st.sidebar:
+    st.title("📝 Summarizer LLM")
+    st.markdown("*Summarize long text with BART Large CNN*")
+    st.markdown("___")
+
+    st.subheader("📊 Session Stats")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.header("Summaries")
+        if "messages" in st.session_state:
+            st.markdown(f"# {len(st.session_state.messages) // 2}")
+        else:
+            st.markdown("# 0")
+    with col2:
+        st.header("Total")
+        if "messages" in st.session_state:
+            st.markdown(f"# {len(st.session_state.messages)}")
+        else:
+            st.markdown("# 0")
+    st.markdown("___")
+
+    st.header("⚙ Controls")
+    max_length = st.slider("Max Length", min_value=50, max_value=500, value=150, step=10)
+    min_length = st.slider("Min Length", min_value=10, max_value=100, value=30, step=5)
+    temperature = st.slider("Temperature", min_value=0.1, max_value=2.0, value=1.0, step=0.1)
+    top_k = st.slider("Top-K", min_value=1, max_value=100, value=50, step=1)
+    top_p = st.slider("Top-P", min_value=0.1, max_value=1.0, value=0.9, step=0.05)
+    st.markdown("___")
+
+# Main Section
+st.subheader("🗯 Summarize Your Text")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+chat_box = st.container(height=500, border=True)
+
+# Display history
+with chat_box:
+    for i, msg in enumerate(st.session_state.messages):
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["text"])
+
+# Input area
+user_input = st.chat_input("Paste your long text here...")
+if user_input:
+
+    st.session_state.messages.append({"role": "user", "text": user_input})
+
+    with st.spinner("AI is summarizing your text..."):
+        try:
+            url = "http://127.0.0.1:8000/summarize" 
+            payload = {
+                "text": user_input,
+                "max_length": max_length,
+                "min_length": min_length,
+                "temperature": temperature,
+                "top_k": top_k,
+                "top_p": top_p,
+            }
+            response = requests.post(url, json=payload)
+            response.raise_for_status()
+            data = response.json()
+            ai_reply = data.get("summary_text", "⚠️ No summary returned.")
+        except requests.exceptions.RequestException as e:
+            ai_reply = f"⚠️ Error: {e}"
+
+    # Save assistant reply
+    st.session_state.messages.append({"role": "assistant", "text": ai_reply})
+    st.rerun()

Exercises/Exercise_3/app.py

@@ -0,0 +1,27 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+
+# Create FastAPI app
+app = FastAPI(title="Sentiment Analysis API")
+
+
+sentiment_model = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+
+# Request body
+class SentimentRequest(BaseModel):
+    text: str
+
+
+@app.post("/sentiment")
+async def analyze_sentiment(request: SentimentRequest):
+    """
+    Analyze the sentiment of a given text (positive/negative).
+    """
+    result = sentiment_model(request.text)[0]  
+    return {
+        "text": request.text,
+        "label": result["label"],  
+        "score": round(result["score"], 4)  
+    }

Exercises/Exercise_3/frontend.py

@@ -0,0 +1,62 @@
+import streamlit as st
+import requests
+
+st.set_page_config(layout="wide", page_title="Sentiment Analyzer", page_icon="😊")
+
+# Sidebar
+with st.sidebar:
+    st.title("😊 Sentiment Analyzer")
+    st.markdown("*detect positive or negative tone*")
+    st.markdown("___")
+
+    st.subheader("📊 Session Stats")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.header("Messages")
+        if "messages" in st.session_state:
+            st.markdown(f"# {len(st.session_state.messages) // 2}")
+        else:
+            st.markdown("# 0")
+    with col2:
+        st.header("Total")
+        if "messages" in st.session_state:
+            st.markdown(f"# {len(st.session_state.messages)}")
+        else:
+            st.markdown("# 0")
+    st.markdown("___")
+
+# Main Chat Section
+st.subheader("🗯 Chat with Sentiment Analyzer")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+chat_box = st.container(height=500, border=True)
+
+# Display chat history
+with chat_box:
+    for i, msg in enumerate(st.session_state.messages):
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["text"])
+
+
+user_input = st.chat_input("Enter a sentence to analyze sentiment...")
+if user_input:
+
+    st.session_state.messages.append({"role": "user", "text": user_input})
+
+
+    with st.spinner("Analyzing sentiment..."):
+        try:
+            url = "http://127.0.0.1:8000/sentiment"  
+            payload = {"text": user_input}
+            response = requests.post(url, json=payload)
+            response.raise_for_status()
+            data = response.json()
+            ai_reply = f"**Sentiment:** {data['label']}  \n**Confidence:** {data['score']}"
+        except requests.exceptions.RequestException as e:
+            ai_reply = f"⚠️ Error: {e}"
+
+    # Save assistant reply
+    st.session_state.messages.append({"role": "assistant", "text": ai_reply})
+    st.rerun()