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alaselababatunde commited on Sep 19

Commit

f51917e

1 Parent(s): 7b32b5c

Updated

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Files changed (1) hide show

main.py +77 -56

main.py CHANGED Viewed

@@ -7,11 +7,11 @@ from spitch import Spitch
 from langchain.prompts import PromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
 from langdetect import detect, DetectorFactory
-from huggingface_hub.utils import HfHubHTTPError  # ✅ for quota error handling
 DetectorFactory.seed = 0
-# --------- BASIC CONFIG ----------
 SPITCH_API_KEY = os.getenv("SPITCH_API_KEY")
 HF_MODEL = os.getenv("HF_MODEL", "google/flan-t5-base")
 FRONTEND_ORIGIN = os.getenv("ALLOWED_ORIGIN", "*")
@@ -24,7 +24,7 @@ if not SPITCH_API_KEY:
 os.environ["SPITCH_API_KEY"] = SPITCH_API_KEY
 spitch_client = Spitch()
-# ✅ HuggingFaceEndpoint
 llm = HuggingFaceEndpoint(
     repo_id=HF_MODEL,
     temperature=0.2,
@@ -43,7 +43,7 @@ app.add_middleware(
     allow_headers=["Authorization", "Content-Type"],
 )
-# --------- PROMPT TEMPLATES ----------
 chat_template = """You are DevAssist, an AI coding assistant.
 Guidelines:
@@ -59,40 +59,43 @@ Question: {question}
 Answer:
 """
 stt_chat_template = """You are DevAssist, an AI coding assistant.
 - The input is transcribed speech. Interpret it as a dev question.
 - Provide clear answers with code examples (use markdown triple backticks).
 - If input is unclear, ask a clarifying question.
 Spoken Question: {speech}
-Answer:"""
 autodoc_template = """You are DevAssist DocBot.
 - Read the code and produce professional documentation in markdown.
 Code: {code}
-Documentation:"""
 sme_template = """
 You are an SME site builder AI.
 Your job is to turn ANY user prompt (simple or complex) into a working, modern web project.
 You must analyze the request carefully and generate clean, professional, and responsive code.
-🔑 Rules:
 1. Always return ONLY valid JSON (no explanations, no Markdown).
 2. Always include "index.html", "style.css", and "script.js" in the "files".
-3. Support multiple pages if the user specifies them in the request.
 4. Style must be modern, vibrant, and responsive:
-   - Use semantic HTML5 structure (<header>, <main>, <section>, <footer>).
-   - Use CSS with modern fonts, colors, spacing, hover effects, flex/grid layout.
    - Buttons must be styled and interactive.
    - Add responsiveness for mobile.
 5. Translate vague descriptions like “make it futuristic and sharp” into concrete CSS design choices.
-6. If the user’s request is very detailed, honor as much as possible while still keeping valid code.
 User Prompt:
 {user_prompt}
-Return ONLY JSON in this exact format:
 {
   "files": {
     "index.html": "<!DOCTYPE html> ... </html>",
@@ -103,29 +106,20 @@ Return ONLY JSON in this exact format:
 }
 """
-# --------- CHAINS ----------
-chat_prompt = PromptTemplate(input_variables=["question"], template=chat_template)
-stt_prompt = PromptTemplate(input_variables=["speech"], template=stt_chat_template)
-autodoc_prompt = PromptTemplate(input_variables=["code"], template=autodoc_template)
-sme_prompt = PromptTemplate(
-    input_variables=["site_name", "stack", "pages", "language"],
-    template=sme_template
-)
-chat_chain = chat_prompt | llm
-stt_chain = stt_prompt | llm
-autodoc_chain = autodoc_prompt | llm
-sme_chain = sme_prompt | llm
-# --------- REQUEST MODELS ----------
 class ChatRequest(BaseModel):
     question: str
 class AutoDocRequest(BaseModel):
     code: str
-# --------- AUTH ----------
 def check_auth(authorization: str | None):
     if not PROJECT_API_KEY:
         return
@@ -135,7 +129,7 @@ def check_auth(authorization: str | None):
     if token != PROJECT_API_KEY:
         raise HTTPException(status_code=403, detail="Invalid token")
-# --------- ENDPOINTS ----------
 @app.get("/")
 def root():
     return {"status": "DevAssist AI Backend running"}
@@ -148,9 +142,7 @@ def chat(req: ChatRequest, authorization: str | None = Header(None)):
         return {"reply": answer.strip() if isinstance(answer, str) else str(answer)}
     except HfHubHTTPError as e:
         if "exceeded" in str(e).lower() or "quota" in str(e).lower():
-            return {
-                "reply": "⚠️ You have reached your daily limit for reponses today. Please come back in 24 hours."
-            }
         raise e
 @app.post("/stt")
@@ -171,7 +163,7 @@ async def stt_audio(file: UploadFile = File(...), lang_hint: str | None = None,
     except Exception:
         resp = spitch_client.speech.transcribe(language="en", content=open(tmp_path, "rb").read())
-    transcription = getattr(resp, "text", "") or resp.get("text", "") if isinstance(resp, dict) else ""
     try:
         detected_lang = detect(transcription) if transcription.strip() else "en"
@@ -182,7 +174,7 @@ async def stt_audio(file: UploadFile = File(...), lang_hint: str | None = None,
     if detected_lang != "en":
         try:
             translation_resp = spitch_client.text.translate(text=transcription, source=detected_lang, target="en")
-            translation = getattr(translation_resp, "text", "") or translation_resp.get("text", "") if isinstance(translation_resp, dict) else translation
         except Exception:
             translation = transcription
@@ -203,33 +195,62 @@ def autodoc(req: AutoDocRequest, authorization: str | None = Header(None)):
 @app.post("/sme/generate")
 async def sme_generate(payload: dict = Body(...)):
-    """
-    Generate SME site boilerplate.
-    Expected payload:
-    {
-      "site_name": "...",
-      "stack": "react"|"html-css-js",
-      "pages": [{ "name": "home", "content": "..." }],
-      "language": "en"
-    }
-    """
     try:
-        response = sme_chain.invoke({
-            "site_name": payload.get("site_name"),
-            "stack": payload.get("stack"),
-            "pages": payload.get("pages"),
-            "language": payload.get("language"),
-        })
         return {"success": True, "data": response}
     except HfHubHTTPError as e:
         if "exceeded" in str(e).lower() or "quota" in str(e).lower():
-            return {
-                "success": False,
-                "error": "⚠️ Token quota for today has been used. Please come back in 24 hours."
-            }
         raise e
-# ✅ Hugging Face requires port 7860, not 8000
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=False)

 from langchain.prompts import PromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
 from langdetect import detect, DetectorFactory
+from huggingface_hub.utils import HfHubHTTPError  # for quota error handling
+# ----------------- CONFIG -----------------
 DetectorFactory.seed = 0
 SPITCH_API_KEY = os.getenv("SPITCH_API_KEY")
 HF_MODEL = os.getenv("HF_MODEL", "google/flan-t5-base")
 FRONTEND_ORIGIN = os.getenv("ALLOWED_ORIGIN", "*")
 os.environ["SPITCH_API_KEY"] = SPITCH_API_KEY
 spitch_client = Spitch()
+# HuggingFace LLM
 llm = HuggingFaceEndpoint(
     repo_id=HF_MODEL,
     temperature=0.2,
     allow_headers=["Authorization", "Content-Type"],
 )
+# ----------------- PROMPT TEMPLATES -----------------
 chat_template = """You are DevAssist, an AI coding assistant.
 Guidelines:
 Answer:
 """
 stt_chat_template = """You are DevAssist, an AI coding assistant.
 - The input is transcribed speech. Interpret it as a dev question.
 - Provide clear answers with code examples (use markdown triple backticks).
 - If input is unclear, ask a clarifying question.
 Spoken Question: {speech}
+Answer:
+"""
 autodoc_template = """You are DevAssist DocBot.
 - Read the code and produce professional documentation in markdown.
 Code: {code}
+Documentation:
+"""
 sme_template = """
 You are an SME site builder AI.
 Your job is to turn ANY user prompt (simple or complex) into a working, modern web project.
 You must analyze the request carefully and generate clean, professional, and responsive code.
+Rules:
 1. Always return ONLY valid JSON (no explanations, no Markdown).
 2. Always include "index.html", "style.css", and "script.js" in the "files".
+3. Support multiple pages if the user specifies them.
 4. Style must be modern, vibrant, and responsive:
+   - Use semantic HTML5 (<header>, <main>, <section>, <footer>).
+   - Use CSS with modern fonts, colors, spacing, hover effects, flex/grid.
    - Buttons must be styled and interactive.
    - Add responsiveness for mobile.
 5. Translate vague descriptions like “make it futuristic and sharp” into concrete CSS design choices.
+6. If the request is very detailed, honor as much as possible while still keeping valid code.
 User Prompt:
 {user_prompt}
+Return ONLY JSON in this format:
 {
   "files": {
     "index.html": "<!DOCTYPE html> ... </html>",
 }
 """
+# ----------------- CHAINS -----------------
+chat_chain = PromptTemplate(input_variables=["question"], template=chat_template) | llm
+stt_chain = PromptTemplate(input_variables=["speech"], template=stt_chat_template) | llm
+autodoc_chain = PromptTemplate(input_variables=["code"], template=autodoc_template) | llm
+sme_chain = PromptTemplate(input_variables=["user_prompt"], template=sme_template) | llm
+# ----------------- REQUEST MODELS -----------------
 class ChatRequest(BaseModel):
     question: str
 class AutoDocRequest(BaseModel):
     code: str
+# ----------------- AUTH -----------------
 def check_auth(authorization: str | None):
     if not PROJECT_API_KEY:
         return
     if token != PROJECT_API_KEY:
         raise HTTPException(status_code=403, detail="Invalid token")
+# ----------------- ENDPOINTS -----------------
 @app.get("/")
 def root():
     return {"status": "DevAssist AI Backend running"}
         return {"reply": answer.strip() if isinstance(answer, str) else str(answer)}
     except HfHubHTTPError as e:
         if "exceeded" in str(e).lower() or "quota" in str(e).lower():
+            return {"reply": "⚠️ You have reached your daily limit for responses. Please come back in 24 hours."}
         raise e
 @app.post("/stt")
     except Exception:
         resp = spitch_client.speech.transcribe(language="en", content=open(tmp_path, "rb").read())
+    transcription = getattr(resp, "text", "") or (resp.get("text", "") if isinstance(resp, dict) else "")
     try:
         detected_lang = detect(transcription) if transcription.strip() else "en"
     if detected_lang != "en":
         try:
             translation_resp = spitch_client.text.translate(text=transcription, source=detected_lang, target="en")
+            translation = getattr(translation_resp, "text", "") or translation_resp.get("text", "")
         except Exception:
             translation = transcription
 @app.post("/sme/generate")
 async def sme_generate(payload: dict = Body(...)):
     try:
+        response = sme_chain.invoke({"user_prompt": payload.get("user_prompt", "")})
         return {"success": True, "data": response}
     except HfHubHTTPError as e:
         if "exceeded" in str(e).lower() or "quota" in str(e).lower():
+            return {"success": False, "error": "⚠️ Token quota for today has been used. Please come back in 24 hours."}
+        raise e
+@app.post("/sme/speech-generate")
+async def sme_speech_generate(file: UploadFile = File(...), lang_hint: str | None = None, authorization: str | None = Header(None)):
+    check_auth(authorization)
+    suffix = os.path.splitext(file.filename)[1] or ".wav"
+    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tf:
+        content = await file.read()
+        tf.write(content)
+        tmp_path = tf.name
+    try:
+        if lang_hint:
+            resp = spitch_client.speech.transcribe(language=lang_hint, content=open(tmp_path, "rb").read())
+        else:
+            resp = spitch_client.speech.transcribe(content=open(tmp_path, "rb").read())
+    except Exception:
+        resp = spitch_client.speech.transcribe(language="en", content=open(tmp_path, "rb").read())
+    transcription = getattr(resp, "text", "") or (resp.get("text", "") if isinstance(resp, dict) else "")
+    try:
+        detected_lang = detect(transcription) if transcription.strip() else "en"
+    except Exception:
+        detected_lang = "en"
+    translation = transcription
+    if detected_lang != "en":
+        try:
+            translation_resp = spitch_client.text.translate(text=transcription, source=detected_lang, target="en")
+            translation = getattr(translation_resp, "text", "") or translation_resp.get("text", "")
+        except Exception:
+            translation = transcription
+    try:
+        sme_response = sme_chain.invoke({"user_prompt": translation})
+        return {
+            "success": True,
+            "transcription": transcription,
+            "detected_language": detected_lang,
+            "translation": translation,
+            "sme_site": sme_response
+        }
+    except HfHubHTTPError as e:
+        if "exceeded" in str(e).lower() or "quota" in str(e).lower():
+            return {"success": False, "error": "⚠️ Token quota for today has been used. Please come back in 24 hours."}
         raise e
+# Hugging Face requires port 7860
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=False)