Update app.py

app.py

@@ -1,86 +1,148 @@
 # app.py
 
+import os
 import requests
 import wikipedia
 import gradio as gr
-from transformers import pipeline
+import torch
+from transformers import (
+    SeamlessM4TProcessor,
+    SeamlessM4TForTextToText,
+    pipeline as hf_pipeline
+)
+
+# ————————————————————
+# 1) SeamlessM4T Text2Text
+MODEL_NAME = "facebook/hf-seamless-m4t-medium"
+device     = "cuda" if torch.cuda.is_available() else "cpu"
+processor  = SeamlessM4TProcessor.from_pretrained(MODEL_NAME)
+m4t_model  = SeamlessM4TForTextToText.from_pretrained(MODEL_NAME).to(device).eval()
+
+def translate_m4t(text, src_iso3, tgt_iso3, auto_detect=False):
+    # src_iso3: e.g. "eng", "fra", etc.  If auto_detect=True, pass None
+    src = None if auto_detect else src_iso3
+    inputs = processor(text=text, src_lang=src, return_tensors="pt").to(device)
+    tokens = m4t_model.generate(**inputs, tgt_lang=tgt_iso3)
+    return processor.decode(tokens[0].tolist(), skip_special_tokens=True)
 
-# 1) Load the BERT NER pipeline
-ner = pipeline(
+# ————————————————————
+# 2) BERT‐based NER
+ner = hf_pipeline(
     "ner",
     model="dslim/bert-base-NER-uncased",
     grouped_entities=True
 )
 
-# 2) Geocode via Nominatim
+# ————————————————————
+# 3) Geocoding & POIs via OpenStreetMap
 def geocode(place: str):
     resp = requests.get(
         "https://nominatim.openstreetmap.org/search",
         params={"q": place, "format": "json", "limit": 1},
-        headers={"User-Agent": "iVoiceContext/1.0"}
-    )
-    data = resp.json()
-    if not data:
-        return None
-    return float(data[0]["lat"]), float(data[0]["lon"])
+        headers={"User-Agent":"iVoiceContext/1.0"}
+    ).json()
+    if not resp: return None
+    return float(resp[0]["lat"]), float(resp[0]["lon"])
 
-# 3) Fetch POIs via Overpass
 def fetch_osm(lat, lon, osm_filter, limit=5):
     query = f"""
     [out:json][timeout:25];
     (
       node{osm_filter}(around:1000,{lat},{lon});
       way{osm_filter}(around:1000,{lat},{lon});
-      rel{osm_filter}(around:1000,{lat},{lon});
     );
     out center {limit};
     """
     r = requests.post("https://overpass-api.de/api/interpreter", data={"data": query})
     elems = r.json().get("elements", [])
-    items = []
-    for e in elems:
-        name = e.get("tags", {}).get("name")
-        if name:
-            items.append({"name": name})
-    return items
+    return [
+      {"name": e["tags"].get("name", "")}
+      for e in elems
+      if e.get("tags", {}).get("name")
+    ]
+
+# ————————————————————
+def get_context(text: str,
+                source_lang: str,  # always 3-letter, e.g. "eng"
+                output_lang: str,  # always 3-letter, e.g. "fra"
+                auto_detect: bool):
+    # 1) Ensure English for NER
+    if auto_detect or source_lang != "eng":
+        en_text = translate_m4t(text, source_lang, "eng", auto_detect=auto_detect)
+    else:
+        en_text = text
+
+    # 2) Extract entities
+    ner_out = ner(en_text)
+    ents = { ent["word"]: ent["entity_group"] for ent in ner_out }
 
-# 4) Main function
-def get_context(text: str):
-    results = ner(text)
-    # de-duplicate entities by text
-    ents = {ent["word"]: ent["entity_group"] for ent in results}
-    out = {}
-    for word, label in ents.items():
+    results = {}
+    for ent_text, label in ents.items():
         if label == "LOC":
-            geo = geocode(word)
+            geo = geocode(ent_text)
             if not geo:
-                out[word] = {"type":"location", "error":"could not geocode"}
+                results[ent_text] = {"type":"location","error":"could not geocode"}
             else:
                 lat, lon = geo
-                out[word] = {
-                    "type": "location",
-                    "restaurants": fetch_osm(lat, lon, '["amenity"="restaurant"]'),
-                    "attractions": fetch_osm(lat, lon, '["tourism"="attraction"]'),
+                rest = fetch_osm(lat, lon, '["amenity"="restaurant"]')
+                attr = fetch_osm(lat, lon, '["tourism"="attraction"]')
+                results[ent_text] = {
+                    "type":        "location",
+                    "restaurants": rest,
+                    "attractions": attr
                 }
         else:
-            # for PERSON, ORG, MISC, etc → Wikipedia
+            # PERSON, ORG, MISC → Wikipedia
             try:
-                summary = wikipedia.summary(word, sentences=2)
+                summary = wikipedia.summary(ent_text, sentences=2)
             except Exception:
                 summary = "No summary available."
-            out[word] = {"type": "wiki", "summary": summary}
-    if not out:
-        return {"error": "No entities found"}
-    return out
+            results[ent_text] = {"type":"wiki","summary": summary}
 
-# 5) Gradio UI
+    if not results:
+        return {"error":"no entities found"}
+
+    # 3) Translate **all** text fields → output_lang
+    if output_lang != "eng":
+        for info in results.values():
+            if info["type"] == "wiki":
+                info["summary"] = translate_m4t(
+                    info["summary"], "eng", output_lang, auto_detect=False
+                )
+            elif info["type"] == "location":
+                for poi_list in ("restaurants","attractions"):
+                    translated = []
+                    for item in info[poi_list]:
+                        name = item["name"]
+                        tr = translate_m4t(name, "eng", output_lang, auto_detect=False)
+                        translated.append({"name": tr})
+                    info[poi_list] = translated
+
+    return results
+
+# ————————————————————
 iface = gr.Interface(
     fn=get_context,
-    inputs=gr.Textbox(lines=3, placeholder="Paste your translated text…"),
+    inputs=[
+      gr.Textbox(lines=3, placeholder="Enter text…"),
+      gr.Textbox(label="Source Language (ISO 639-3)"),
+      gr.Textbox(label="Target Language (ISO 639-3)"),
+      gr.Checkbox(label="Auto-detect source language")
+    ],
     outputs="json",
-    title="iVoice Context-Aware",
-    description="BERT NER → geocode LOC → Overpass POIs → Wikipedia for others"
-)
+    title="iVoice Translate + Context-Aware",
+    description=(
+      "1) Translate your text → English (if needed)\n"
+      "2) Run BERT-NER on English to find LOC/PERSON/ORG\n"
+      "3) Geocode LOC → fetch nearby restaurants & attractions\n"
+      "4) Fetch Wikipedia summaries for PERSON/ORG\n"
+      "5) Translate **all** results → your target language"
+    )
+).queue()
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch(
+      server_name="0.0.0.0",
+      server_port=int(os.environ.get("PORT", 7860)),
+      share=True
+    )