app.py

app.py

@@ -1,122 +1,121 @@
-import gradio as gr
-from google.colab import drive
-import os
-import pandas as pd
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-import numpy as np
-import faiss
-from sentence_transformers import SentenceTransformer, CrossEncoder
-import openai
-
-csv_path = 'train_data.csv'
-if not os.path.isfile(csv_path):
-    raise FileNotFoundError(f"Could not find CSV at {csv_path}")
-
-df = pd.read_csv(csv_path, on_bad_lines='skip').dropna()
-df.columns = ['Question', 'Answer']
-
-# STEP 3: Build TF-IDF structures (same)
-questions = df['Question'].tolist()
-answers = df['Answer'].tolist()
-qa_pairs = [f"Q: {q}\nA: {a}" for q, a in zip(questions, answers)]
-tfidf = TfidfVectorizer(max_features=5000).fit(questions)
-tfidf_matrix = tfidf.transform(questions)
-
-# STEP 4: Enhanced Embedding of Q+A pairs
-embedder = SentenceTransformer("all-mpnet-base-v2")
-qa_embeddings = embedder.encode(qa_pairs, convert_to_numpy=True)
-dim = qa_embeddings.shape[1]
-index = faiss.IndexHNSWFlat(dim, 32)
-index.hnsw.efConstruction = 200
-index.add(qa_embeddings)
-
-# STEP 5: Together AI Setup (same)
-openai.api_key = "cfbafb6a338787841b0295fa7fbe0e4acca77b70ccc3d92bafea2004783b93a3"
-openai.api_base = "https://api.together.xyz/v1"
-
-# STEP 6: Smarter Hybrid Context Retriever
-cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-12-v2")
-
-def get_top_k_matches(query, lex_n=50, sem_k=20, ce_k=5):
-    # Lexical filter
-    q_tfidf = tfidf.transform([query])
-    lex_scores = cosine_similarity(q_tfidf, tfidf_matrix).flatten()
-    lex_idxs = np.argsort(lex_scores)[-lex_n:][::-1]
-
-    # Embed query
-    q_emb = embedder.encode([query], convert_to_numpy=True)
-    sub_embs = qa_embeddings[lex_idxs]
-    dists = np.linalg.norm(sub_embs - q_emb, axis=1)
-    top_sem_idxs = np.argsort(dists)[:sem_k]
-    cand_idxs = [lex_idxs[i] for i in top_sem_idxs]
-
-    # Cross-encoder for precision rerank
-    candidates = [qa_pairs[i] for i in cand_idxs]
-    pairs = [[query, cand] for cand in candidates]
-    ce_scores = cross_encoder.predict(pairs)
-    scored = sorted(zip(ce_scores, candidates), reverse=True)
-    top_contexts = [ctx for _, ctx in scored[:ce_k]]
-    return top_contexts
-
-# STEP 7: Smart Prompt Generator (unchanged)
-def generate_prompt(user_query, context):
-    return f"""
-You are a smart and friendly assistant helping students with academic-related queries.
-
-Below is a question from a student. You have been given multiple pieces of relevant academic context pulled from the official college documentation. Carefully analyze all the given Q&A context and generate the most accurate, clear, and helpful answer for the student.
-
-### Student's Question:
-{user_query}
-
-### Top Contexts:
-{context}
-
-### Instructions:
-- Use all relevant context to form your answer.
-- Avoid repeating the same sentences. Summarize smartly.
-- Keep your answer polite and student-friendly.
-- If not found, reply: "I'm sorry, I couldn't find this information in the provided academic context."
-
-### Your Final Answer:
-"""
-
-# STEP 8: Ask a question and get response (unchanged)
-def ask_bot(question):
-    context = get_top_k_matches(question)
-    prompt = generate_prompt(question, context)
-    response = openai.ChatCompletion.create(
-        model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
-        messages=[{"role":"user","content":prompt}],
-        temperature=0.5, max_tokens=1024
-    )
-    return response.choices[0].message.content
-
-
-# Define query function
-def qa_pipeline(query, history=[]):
-    try:
-        response = ask_bot(query)
-        history.append((query, response))
-        return "", history
-    except Exception as e:
-        history.append((query, f"⚠️ Error: {str(e)}"))
-        return "", history
-
-# Launch UI with blocks
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 🤖 KCT Smart Chatbot")
-    gr.Markdown("Ask academic or college-related questions. Powered by your custom dataset.")
-
-    chatbot = gr.Chatbot(label="KCT Chatbot", height=400)
-    msg = gr.Textbox(label="Enter your question here")
-    clear = gr.Button("🧹 Clear Chat")
-
-    # On send
-    def user_submit(user_input, chat_history):
-        return qa_pipeline(user_input, chat_history)
-
-    msg.submit(user_submit, [msg, chatbot], [msg, chatbot])
-    clear.click(lambda: None, None, chatbot, queue=False)
-
+import gradio as gr
+import os
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import faiss
+from sentence_transformers import SentenceTransformer, CrossEncoder
+import openai
+
+csv_path = 'train_data.csv'
+if not os.path.isfile(csv_path):
+    raise FileNotFoundError(f"Could not find CSV at {csv_path}")
+
+df = pd.read_csv(csv_path, on_bad_lines='skip').dropna()
+df.columns = ['Question', 'Answer']
+
+# STEP 3: Build TF-IDF structures (same)
+questions = df['Question'].tolist()
+answers = df['Answer'].tolist()
+qa_pairs = [f"Q: {q}\nA: {a}" for q, a in zip(questions, answers)]
+tfidf = TfidfVectorizer(max_features=5000).fit(questions)
+tfidf_matrix = tfidf.transform(questions)
+
+# STEP 4: Enhanced Embedding of Q+A pairs
+embedder = SentenceTransformer("all-mpnet-base-v2")
+qa_embeddings = embedder.encode(qa_pairs, convert_to_numpy=True)
+dim = qa_embeddings.shape[1]
+index = faiss.IndexHNSWFlat(dim, 32)
+index.hnsw.efConstruction = 200
+index.add(qa_embeddings)
+
+# STEP 5: Together AI Setup (same)
+openai.api_key = "cfbafb6a338787841b0295fa7fbe0e4acca77b70ccc3d92bafea2004783b93a3"
+openai.api_base = "https://api.together.xyz/v1"
+
+# STEP 6: Smarter Hybrid Context Retriever
+cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-12-v2")
+
+def get_top_k_matches(query, lex_n=50, sem_k=20, ce_k=5):
+    # Lexical filter
+    q_tfidf = tfidf.transform([query])
+    lex_scores = cosine_similarity(q_tfidf, tfidf_matrix).flatten()
+    lex_idxs = np.argsort(lex_scores)[-lex_n:][::-1]
+
+    # Embed query
+    q_emb = embedder.encode([query], convert_to_numpy=True)
+    sub_embs = qa_embeddings[lex_idxs]
+    dists = np.linalg.norm(sub_embs - q_emb, axis=1)
+    top_sem_idxs = np.argsort(dists)[:sem_k]
+    cand_idxs = [lex_idxs[i] for i in top_sem_idxs]
+
+    # Cross-encoder for precision rerank
+    candidates = [qa_pairs[i] for i in cand_idxs]
+    pairs = [[query, cand] for cand in candidates]
+    ce_scores = cross_encoder.predict(pairs)
+    scored = sorted(zip(ce_scores, candidates), reverse=True)
+    top_contexts = [ctx for _, ctx in scored[:ce_k]]
+    return top_contexts
+
+# STEP 7: Smart Prompt Generator (unchanged)
+def generate_prompt(user_query, context):
+    return f"""
+You are a smart and friendly assistant helping students with academic-related queries.
+
+Below is a question from a student. You have been given multiple pieces of relevant academic context pulled from the official college documentation. Carefully analyze all the given Q&A context and generate the most accurate, clear, and helpful answer for the student.
+
+### Student's Question:
+{user_query}
+
+### Top Contexts:
+{context}
+
+### Instructions:
+- Use all relevant context to form your answer.
+- Avoid repeating the same sentences. Summarize smartly.
+- Keep your answer polite and student-friendly.
+- If not found, reply: "I'm sorry, I couldn't find this information in the provided academic context."
+
+### Your Final Answer:
+"""
+
+# STEP 8: Ask a question and get response (unchanged)
+def ask_bot(question):
+    context = get_top_k_matches(question)
+    prompt = generate_prompt(question, context)
+    response = openai.ChatCompletion.create(
+        model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        messages=[{"role":"user","content":prompt}],
+        temperature=0.5, max_tokens=1024
+    )
+    return response.choices[0].message.content
+
+
+# Define query function
+def qa_pipeline(query, history=[]):
+    try:
+        response = ask_bot(query)
+        history.append((query, response))
+        return "", history
+    except Exception as e:
+        history.append((query, f"⚠️ Error: {str(e)}"))
+        return "", history
+
+# Launch UI with blocks
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 🤖 KCT Smart Chatbot")
+    gr.Markdown("Ask academic or college-related questions. Powered by your custom dataset.")
+
+    chatbot = gr.Chatbot(label="KCT Chatbot", height=400)
+    msg = gr.Textbox(label="Enter your question here")
+    clear = gr.Button("🧹 Clear Chat")
+
+    # On send
+    def user_submit(user_input, chat_history):
+        return qa_pipeline(user_input, chat_history)
+
+    msg.submit(user_submit, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
 demo.launch(share=True)