Update app.py

app.py

@@ -201,29 +201,47 @@ def analyze_message(text):
 def analyze_composite(uploaded_file, *texts):
     outputs = []
 
-    # 1) File upload
+    # 1) Handle an uploaded file
     if uploaded_file is not None:
-        name = (uploaded_file.name if hasattr(uploaded_file, "name") else uploaded_file).lower()
-    if name.endswith((".png",".jpg",".jpeg",".tiff",".bmp",".gif")):
-        img = Image.open(io.BytesIO(raw))
-        arr = np.array(img.convert("RGB"))
-        texts = ocr_reader.readtext(arr, detail=0)
-        content = "\n".join(texts)
-    else:
+        # uploaded_file may be a file-like object or just a path string
         try:
-            content = raw.decode("utf-8")
-        except:
-            content = raw.decode("latin-1")
-
-    r = analyze_message(content)
-    outputs.append(
-        "── Uploaded File ──\n"
-        f"Emotion Profile : {r['emotion_profile']}\n"
-        f"Active Patterns : {r['active_patterns']}\n"
-        f"Emotional Tone  : {r['tone_tag']}\n"
+            raw = uploaded_file.read()
+        except Exception:
+            # fall back to opening the path
+            with open(uploaded_file, "rb") as f:
+                raw = f.read()
+
+        # get the filename (or just lowercase the string if it's a path)
+        name = (
+            uploaded_file.name.lower()
+            if hasattr(uploaded_file, "name")
+            else uploaded_file.lower()
         )
 
-    # 2) Text‐box inputs…
+        # branch on extension
+        if name.endswith((".png", ".jpg", ".jpeg", ".tiff", ".bmp", ".gif")):
+            img = Image.open(io.BytesIO(raw))
+            # --- using easyocr or pytesseract, e.g. with easyocr: ---
+            # arr = np.array(img.convert("RGB"))
+            # texts = ocr_reader.readtext(arr, detail=0)
+            # content = "\n".join(texts)
+            content = pytesseract.image_to_string(img)
+        else:
+            try:
+                content = raw.decode("utf-8")
+            except UnicodeDecodeError:
+                content = raw.decode("latin-1")
+
+        # now analyze that extracted content
+        r = analyze_message(content)
+        outputs.append(
+            "── Uploaded File ──\n"
+            f"Emotion Profile : {r['emotion_profile']}\n"
+            f"Active Patterns : {r['active_patterns']}\n"
+            f"Emotional Tone  : {r['tone_tag']}\n"
+        )
+
+    # 2) Handle any text‐box inputs
     for idx, txt in enumerate(texts, start=1):
         if not txt:
             continue
@@ -234,10 +252,13 @@ def analyze_composite(uploaded_file, *texts):
             f"Active Patterns : {r['active_patterns']}\n"
             f"Emotional Tone  : {r['tone_tag']}\n"
         )
+
+    # 3) No input guard
     if not outputs:
         return "Please enter at least one message."
-    return "\n".join(outputs)
 
+    # 4) Return combined results
+    return "\n".join(outputs)
 # ——— 6) Gradio Interface ————————————————————————————————————————————————
 message_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(1)]
 iface = gr.Interface(