Update app.py

app.py

@@ -1,161 +1,210 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from TextToSpeech import text_to_speech
-import torch
-import base64
-
-# =========================================
-# ENUM MAPPINGS (Match Backend Enums)
-# =========================================
-
-SESSION_TYPES = {
-    1: "technical",
-    2: "softskills"
-}
-
-TRACKS = {
-    19: "generalprogramming"
-}
-
-# =========================================
-# LOAD MODEL ONCE (Global)
-# =========================================
-
-MODEL_PATH = "Fayza38/Question_and_Answer"
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
-
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_PATH,
-    torch_dtype=torch.float32,
-    device_map="cpu"
-)
-
-app = FastAPI()
-
-
-# =========================================
-# REQUEST MODEL
-# =========================================
-
-class QuestionRequest(BaseModel):
-    sessionType: int
-    difficultyLevel: int | None = None
-    trackName: int
-
-
-# =========================================
-# HELPER: GENERATE TEXT USING QWEN TEMPLATE
-# =========================================
-
-def generate_from_model(prompt: str):
-
-    messages = [
-        {"role": "system", "content": "You are a professional interview question generator."},
-        {"role": "user", "content": prompt}
-    ]
-
-    formatted_prompt = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
-
-    inputs = tokenizer(formatted_prompt, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=1200,
-            temperature=0.7
-        )
-
-    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    return decoded
-
-
-# =========================================
-# PARSE Q/A FORMAT
-# =========================================
-
-def parse_qa_blocks(text: str):
-
-    blocks = text.split("\n\n")
-    results = []
-
-    for block in blocks:
-        if "Q:" in block and "A:" in block:
-            parts = block.split("A:")
-            question = parts[0].replace("Q:", "").strip()
-            answer = parts[1].strip()
-            results.append((question, answer))
-
-    return results
-
-
-
-
-# =========================================
-# MAIN ENDPOINT
-# =========================================
-
-@app.post("/generate-questions")
-def generate_questions(request: QuestionRequest):
-
-    if request.sessionType not in SESSION_TYPES:
-        raise HTTPException(status_code=400, detail="Invalid session type")
-
-    session_type = SESSION_TYPES[request.sessionType]
-
-    # ---------------- SOFT SKILLS ----------------
-
-    if session_type == "softskills":
-
-        prompt = """
-Generate 10 behavioral interview questions.
-Format exactly as:
-Q: ...
-A: ...
-"""
-
-    # ---------------- TECHNICAL ----------------
-
-    elif session_type == "technical":
-
-        if request.trackName not in TRACKS:
-            raise HTTPException(status_code=400, detail="Track not supported")
-
-        difficulty = request.difficultyLevel or 1
-
-        prompt = f"""
-Generate 10 General Programming interview questions.
-Difficulty level: {difficulty}
-Format exactly as:
-Q: ...
-A: ...
-"""
-
-    else:
-        raise HTTPException(status_code=400, detail="Invalid session type")
-
-    # -------- Generate once --------
-    raw_output = generate_from_model(prompt)
-
-    qa_pairs = parse_qa_blocks(raw_output)
-
-    if len(qa_pairs) == 0:
-        raise HTTPException(status_code=500, detail="Model failed to generate valid Q/A format")
-
-    response = []
-
-    for idx, (question, answer) in enumerate(qa_pairs[:10], 1):
-
-        response.append({
-            "questionText": question,
-            "questionId": idx,
-            "questionIdealAnswer": answer
-        })
-
-    return response
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+MODEL_ID = "Fayza38/Question_and_Answer"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float32,
+    device_map="cpu"
+)
+
+def generate_questions(prompt):
+
+    messages = [
+        {"role": "system", "content": "You are a professional interview question generator."},
+        {"role": "user", "content": prompt}
+    ]
+
+    formatted_prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    inputs = tokenizer(formatted_prompt, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=800,
+            temperature=0.7
+        )
+
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return decoded
+
+
+iface = gr.Interface(
+    fn=generate_questions,
+    inputs="text",
+    outputs="text",
+    title="Interview Question Generator"
+)
+
+iface.launch()
+
+# # =========================================
+# # ENUM MAPPINGS (Match Backend Enums)
+# # =========================================
+
+# SESSION_TYPES = {
+#     1: "technical",
+#     2: "softskills"
+# }
+
+# TRACKS = {
+#     19: "generalprogramming"
+# }
+
+# # =========================================
+# # LOAD MODEL ONCE (Global)
+# # =========================================
+
+# MODEL_PATH = "Fayza38/Question_and_Answer"
+
+# tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+
+# model = AutoModelForCausalLM.from_pretrained(
+#     MODEL_PATH,
+#     torch_dtype=torch.float32,
+#     device_map="cpu"
+# )
+
+# app = FastAPI()
+
+
+# # =========================================
+# # REQUEST MODEL
+# # =========================================
+
+# class QuestionRequest(BaseModel):
+#     sessionType: int
+#     difficultyLevel: int | None = None
+#     trackName: int
+
+
+# # =========================================
+# # HELPER: GENERATE TEXT USING QWEN TEMPLATE
+# # =========================================
+
+# def generate_from_model(prompt: str):
+
+#     messages = [
+#         {"role": "system", "content": "You are a professional interview question generator."},
+#         {"role": "user", "content": prompt}
+#     ]
+
+#     formatted_prompt = tokenizer.apply_chat_template(
+#         messages,
+#         tokenize=False,
+#         add_generation_prompt=True
+#     )
+
+#     inputs = tokenizer(formatted_prompt, return_tensors="pt")
+
+#     with torch.no_grad():
+#         outputs = model.generate(
+#             **inputs,
+#             max_new_tokens=1200,
+#             temperature=0.7
+#         )
+
+#     decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+#     return decoded
+
+
+# # =========================================
+# # PARSE Q/A FORMAT
+# # =========================================
+
+# def parse_qa_blocks(text: str):
+
+#     blocks = text.split("\n\n")
+#     results = []
+
+#     for block in blocks:
+#         if "Q:" in block and "A:" in block:
+#             parts = block.split("A:")
+#             question = parts[0].replace("Q:", "").strip()
+#             answer = parts[1].strip()
+#             results.append((question, answer))
+
+#     return results
+
+
+
+
+# # =========================================
+# # MAIN ENDPOINT
+# # =========================================
+
+# @app.post("/generate-questions")
+# def generate_questions(request: QuestionRequest):
+
+#     if request.sessionType not in SESSION_TYPES:
+#         raise HTTPException(status_code=400, detail="Invalid session type")
+
+#     session_type = SESSION_TYPES[request.sessionType]
+
+#     # ---------------- SOFT SKILLS ----------------
+
+#     if session_type == "softskills":
+
+#         prompt = """
+# Generate 10 behavioral interview questions.
+# Format exactly as:
+# Q: ...
+# A: ...
+# """
+
+#     # ---------------- TECHNICAL ----------------
+
+#     elif session_type == "technical":
+
+#         if request.trackName not in TRACKS:
+#             raise HTTPException(status_code=400, detail="Track not supported")
+
+#         difficulty = request.difficultyLevel or 1
+
+#         prompt = f"""
+# Generate 10 General Programming interview questions.
+# Difficulty level: {difficulty}
+# Format exactly as:
+# Q: ...
+# A: ...
+# """
+
+#     else:
+#         raise HTTPException(status_code=400, detail="Invalid session type")
+
+#     # -------- Generate once --------
+#     raw_output = generate_from_model(prompt)
+
+#     qa_pairs = parse_qa_blocks(raw_output)
+
+#     if len(qa_pairs) == 0:
+#         raise HTTPException(status_code=500, detail="Model failed to generate valid Q/A format")
+
+#     response = []
+
+#     for idx, (question, answer) in enumerate(qa_pairs[:10], 1):
+
+#         response.append({
+#             "questionText": question,
+#             "questionId": idx,
+#             "questionIdealAnswer": answer
+#         })
+
+#     return response