Create app.py

app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+import pandas as pd
+import torch
+from sentence_transformers import SentenceTransformer, util
+
+# Load dataset
+df = pd.read_csv("Chatbot.csv")
+questions = df[df["name"] == "User"]["line"].tolist()
+answers = df[df["name"] == "ECO"]["line"].tolist()
+
+# Load embedding model
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+question_embeddings = embedding_model.encode(questions, convert_to_tensor=True)
+
+def get_best_response(user_input):
+    input_embedding = embedding_model.encode(user_input, convert_to_tensor=True)
+    similarities = util.pytorch_cos_sim(input_embedding, question_embeddings)[0]
+    best_match_idx = torch.argmax(similarities).item()
+    best_match_score = similarities[best_match_idx].item()
+    
+    if best_match_score > 0.7:
+        return answers[best_match_idx]
+    return "I'm sorry, I don't have an answer for that."
+
+# Gradio UI
+iface = gr.Interface(fn=get_best_response, inputs="text", outputs="text", title="🤖 AI Chatbot")
+iface.launch()