Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Title of the app
+st.title("IntelliCor Chat with Falcon Model")
+
+# Sidebar for user instructions
+st.sidebar.title("About")
+st.sidebar.info("This app uses the Falcon model to generate text completions based on your input.")
+
+# Load the Falcon model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "tiiuae/falcon-7b-instruct"  # Change to Falcon-3B if needed
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name, 
+        device_map="auto",  # Automatically map to GPU/CPU
+        torch_dtype="auto"  # Use FP16 if available
+    )
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# User input
+prompt = st.text_area("Enter your prompt:", placeholder="Type something here...")
+
+# Inference settings
+max_tokens = st.slider("Max tokens for completion:", 50, 500, 100)
+temperature = st.slider("Temperature (creativity):", 0.0, 1.5, 0.7)
+
+# Generate response when the user clicks the button
+if st.button("Generate"):
+    if prompt.strip() == "":
+        st.warning("Please enter a valid prompt.")
+    else:
+        # Tokenize the input
+        inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
+
+        # Generate text
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            do_sample=True
+        )
+
+        # Decode and display the response
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.subheader("Generated Text:")
+        st.write(response)