Create app.py

app.py

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+import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
+import torch
+
+# Initialize Streamlit app
+st.set_page_config(page_title="Hugging Face Chat", layout="wide")
+
+# Sidebar: Model controls
+st.sidebar.title("Model Controls")
+model_name = st.sidebar.text_input("Enter Model Name", value="karthikeyan-r/slm-custom-model_6k")
+load_model_button = st.sidebar.button("Load Model")
+clear_conversation_button = st.sidebar.button("Clear Conversation")
+clear_model_button = st.sidebar.button("Clear Model")
+
+# Main UI
+st.title("Chat Conversation UI")
+st.write("Start a conversation with your Hugging Face model.")
+
+# Initialize session states
+if "model" not in st.session_state:
+    st.session_state["model"] = None
+if "tokenizer" not in st.session_state:
+    st.session_state["tokenizer"] = None
+if "qa_pipeline" not in st.session_state:
+    st.session_state["qa_pipeline"] = None
+if "conversation" not in st.session_state:
+    st.session_state["conversation"] = []
+
+# Load Model
+if load_model_button:
+    with st.spinner("Loading model..."):
+        try:
+            # Set up device
+            device = 0 if torch.cuda.is_available() else -1
+            
+            # Load model and tokenizer
+            st.session_state["model"] = T5ForConditionalGeneration.from_pretrained(model_name, cache_dir="./model_cache")
+            st.session_state["tokenizer"] = T5Tokenizer.from_pretrained(model_name, cache_dir="./model_cache")
+            
+            # Initialize pipeline
+            st.session_state["qa_pipeline"] = pipeline(
+                "text2text-generation",
+                model=st.session_state["model"],
+                tokenizer=st.session_state["tokenizer"],
+                device=device
+            )
+            
+            st.success("Model loaded successfully and ready!")
+        except Exception as e:
+            st.error(f"Error loading model: {e}")
+
+# Clear Model
+if clear_model_button:
+    st.session_state["model"] = None
+    st.session_state["tokenizer"] = None
+    st.session_state["qa_pipeline"] = None
+    st.success("Model cleared.")
+
+# Chat Input and Output
+if st.session_state["qa_pipeline"]:
+    user_input = st.text_input("Enter your query:", key="chat_input")
+    if st.button("Send"):
+        if user_input:
+            with st.spinner("Generating response..."):
+                try:
+                    response = st.session_state["qa_pipeline"](user_input, max_length=300)
+                    generated_text = response[0]["generated_text"]
+                    st.session_state["conversation"].append(("You", user_input))
+                    st.session_state["conversation"].append(("Model", generated_text))
+                except Exception as e:
+                    st.error(f"Error generating response: {e}")
+
+    # Display conversation
+    for speaker, message in st.session_state["conversation"]:
+        if speaker == "You":
+            st.text_area("You:", message, key=message + "_you", disabled=True)
+        else:
+            st.text_area("Model:", message, key=message + "_model", disabled=True)
+
+# Clear Conversation
+if clear_conversation_button:
+    st.session_state["conversation"] = []
+    st.success("Conversation cleared.")